Explore tests

View and learn more about the tests available in the ValidMind Library, including code examples and usage of key functions.

In this notebook, we'll dive deep into the utilities available for viewing and understanding the various tests that ValidMind provides through the tests module. Whether you're just getting started or looking for advanced tips, you'll find clear examples and explanations to assist you every step of the way.

Before we go into the details, let's import the describe_test and list_tests functions from the validmind.tests module. These are the two functions that can be used to easily filter through tests and view details for individual tests.

from validmind.tests import (
    describe_test,
    list_tests,
    list_tasks,
    list_tags,
    list_tasks_and_tags,
)

Listing All Tests
Understanding Tags and Task Types
Searching for Specific Tests using tags and tasks
Delving into Test Details with describe_test
Next steps
- Discover more learning resources

Listing All Tests

The list_tests function provides a convenient way to retrieve all available tests in the validmind.tests module. When invoked without any parameters, it returns a pandas DataFrame containing detailed information about each test.

list_tests()

ID	Name	Description	Required Inputs	Params	Tags	Tasks
validmind.data_validation.ACFandPACFPlot	AC Fand PACF Plot	Analyzes time series data using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots to...	['dataset']	{}	['time_series_data', 'forecasting', 'statistical_test', 'visualization']	['regression']
validmind.data_validation.ADF	ADF	Assesses the stationarity of a time series dataset using the Augmented Dickey-Fuller (ADF) test....	['dataset']	{}	['time_series_data', 'statsmodels', 'forecasting', 'statistical_test', 'stationarity']	['regression']
validmind.data_validation.AutoAR	Auto AR	Automatically identifies the optimal Autoregressive (AR) order for a time series using BIC and AIC criteria....	['dataset']	{'max_ar_order': {'type': 'int', 'default': 3}}	['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']	['regression']
validmind.data_validation.AutoMA	Auto MA	Automatically selects the optimal Moving Average (MA) order for each variable in a time series dataset based on...	['dataset']	{'max_ma_order': {'type': 'int', 'default': 3}}	['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']	['regression']
validmind.data_validation.AutoStationarity	Auto Stationarity	Automates Augmented Dickey-Fuller test to assess stationarity across multiple time series in a DataFrame....	['dataset']	{'max_order': {'type': 'int', 'default': 5}, 'threshold': {'type': 'float', 'default': 0.05}}	['time_series_data', 'statsmodels', 'forecasting', 'statistical_test']	['regression']
validmind.data_validation.BivariateScatterPlots	Bivariate Scatter Plots	Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables...	['dataset']	{}	['tabular_data', 'numerical_data', 'visualization']	['classification']
validmind.data_validation.BoxPierce	Box Pierce	Detects autocorrelation in time-series data through the Box-Pierce test to validate model performance....	['dataset']	{}	['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']	['regression']
validmind.data_validation.ChiSquaredFeaturesTable	Chi Squared Features Table	Assesses the statistical association between categorical features and a target variable using the Chi-Squared test....	['dataset']	{'p_threshold': {'type': '_empty', 'default': 0.05}}	['tabular_data', 'categorical_data', 'statistical_test']	['classification']
validmind.data_validation.ClassImbalance	Class Imbalance	Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....	['dataset']	{'min_percent_threshold': {'type': 'int', 'default': 10}}	['tabular_data', 'binary_classification', 'multiclass_classification', 'data_quality']	['classification']
validmind.data_validation.DatasetDescription	Dataset Description	Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset....	['dataset']	{}	['tabular_data', 'time_series_data', 'text_data']	['classification', 'regression', 'text_classification', 'text_summarization']
validmind.data_validation.DatasetSplit	Dataset Split	Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...	['datasets']	{}	['tabular_data', 'time_series_data', 'text_data']	['classification', 'regression', 'text_classification', 'text_summarization']
validmind.data_validation.DescriptiveStatistics	Descriptive Statistics	Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...	['dataset']	{}	['tabular_data', 'time_series_data', 'data_quality']	['classification', 'regression']
validmind.data_validation.DickeyFullerGLS	Dickey Fuller GLS	Assesses stationarity in time series data using the Dickey-Fuller GLS test to determine the order of integration....	['dataset']	{}	['time_series_data', 'forecasting', 'unit_root_test']	['regression']
validmind.data_validation.Duplicates	Duplicates	Tests dataset for duplicate entries, ensuring model reliability via data quality verification....	['dataset']	{'min_threshold': {'type': '_empty', 'default': 1}}	['tabular_data', 'data_quality', 'text_data']	['classification', 'regression']
validmind.data_validation.EngleGrangerCoint	Engle Granger Coint	Assesses the degree of co-movement between pairs of time series data using the Engle-Granger cointegration test....	['dataset']	{'threshold': {'type': 'float', 'default': 0.05}}	['time_series_data', 'statistical_test', 'forecasting']	['regression']
validmind.data_validation.FeatureTargetCorrelationPlot	Feature Target Correlation Plot	Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar...	['dataset']	{'fig_height': {'type': '_empty', 'default': 600}}	['tabular_data', 'visualization', 'correlation']	['classification', 'regression']
validmind.data_validation.HighCardinality	High Cardinality	Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....	['dataset']	{'num_threshold': {'type': 'int', 'default': 100}, 'percent_threshold': {'type': 'float', 'default': 0.1}, 'threshold_type': {'type': 'str', 'default': 'percent'}}	['tabular_data', 'data_quality', 'categorical_data']	['classification', 'regression']
validmind.data_validation.HighPearsonCorrelation	High Pearson Correlation	Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....	['dataset']	{'max_threshold': {'type': 'float', 'default': 0.3}, 'top_n_correlations': {'type': 'int', 'default': 10}, 'feature_columns': {'type': 'list', 'default': None}}	['tabular_data', 'data_quality', 'correlation']	['classification', 'regression']
validmind.data_validation.IQROutliersBarPlot	IQR Outliers Bar Plot	Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method....	['dataset']	{'threshold': {'type': 'float', 'default': 1.5}, 'fig_width': {'type': 'int', 'default': 800}}	['tabular_data', 'visualization', 'numerical_data']	['classification', 'regression']
validmind.data_validation.IQROutliersTable	IQR Outliers Table	Determines and summarizes outliers in numerical features using the Interquartile Range method....	['dataset']	{'threshold': {'type': 'float', 'default': 1.5}}	['tabular_data', 'numerical_data']	['classification', 'regression']
validmind.data_validation.IsolationForestOutliers	Isolation Forest Outliers	Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....	['dataset']	{'random_state': {'type': 'int', 'default': 0}, 'contamination': {'type': 'float', 'default': 0.1}, 'feature_columns': {'type': 'list', 'default': None}}	['tabular_data', 'anomaly_detection']	['classification']
validmind.data_validation.JarqueBera	Jarque Bera	Assesses normality of dataset features in an ML model using the Jarque-Bera test....	['dataset']	{}	['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']	['classification', 'regression']
validmind.data_validation.KPSS	KPSS	Assesses the stationarity of time-series data in a machine learning model using the KPSS unit root test....	['dataset']	{}	['time_series_data', 'stationarity', 'unit_root_test', 'statsmodels']	['data_validation']
validmind.data_validation.LJungBox	L Jung Box	Assesses autocorrelations in dataset features by performing a Ljung-Box test on each feature....	['dataset']	{}	['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']	['regression']
validmind.data_validation.LaggedCorrelationHeatmap	Lagged Correlation Heatmap	Assesses and visualizes correlation between target variable and lagged independent variables in a time-series...	['dataset']	{'num_lags': {'type': 'int', 'default': 10}}	['time_series_data', 'visualization']	['regression']
validmind.data_validation.MissingValues	Missing Values	Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....	['dataset']	{'min_threshold': {'type': 'int', 'default': 1}}	['tabular_data', 'data_quality']	['classification', 'regression']
validmind.data_validation.MissingValuesBarPlot	Missing Values Bar Plot	Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on...	['dataset']	{'threshold': {'type': 'int', 'default': 80}, 'fig_height': {'type': 'int', 'default': 600}}	['tabular_data', 'data_quality', 'visualization']	['classification', 'regression']
validmind.data_validation.MutualInformation	Mutual Information	Calculates mutual information scores between features and target variable to evaluate feature relevance....	['dataset']	{'min_threshold': {'type': 'float', 'default': 0.01}, 'task': {'type': 'str', 'default': 'classification'}}	['feature_selection', 'data_analysis']	['classification', 'regression']
validmind.data_validation.PearsonCorrelationMatrix	Pearson Correlation Matrix	Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....	['dataset']	{}	['tabular_data', 'numerical_data', 'correlation']	['classification', 'regression']
validmind.data_validation.PhillipsPerronArch	Phillips Perron Arch	Assesses the stationarity of time series data in each feature of the ML model using the Phillips-Perron test....	['dataset']	{}	['time_series_data', 'forecasting', 'statistical_test', 'unit_root_test']	['regression']
validmind.data_validation.ProtectedClassesDescription	Protected Classes Description	Visualizes the distribution of protected classes in the dataset relative to the target variable...	['dataset']	{'protected_classes': {'type': '_empty', 'default': None}}	['bias_and_fairness', 'descriptive_statistics']	['classification', 'regression']
validmind.data_validation.RollingStatsPlot	Rolling Stats Plot	Evaluates the stationarity of time series data by plotting its rolling mean and standard deviation over a specified...	['dataset']	{'window_size': {'type': 'int', 'default': 12}}	['time_series_data', 'visualization', 'stationarity']	['regression']
validmind.data_validation.RunsTest	Runs Test	Executes Runs Test on ML model to detect non-random patterns in output data sequence....	['dataset']	{}	['tabular_data', 'statistical_test', 'statsmodels']	['classification', 'regression']
validmind.data_validation.ScatterPlot	Scatter Plot	Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices....	['dataset']	{}	['tabular_data', 'visualization']	['classification', 'regression']
validmind.data_validation.ScoreBandDefaultRates	Score Band Default Rates	Analyzes default rates and population distribution across credit score bands....	['dataset', 'model']	{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}}	['visualization', 'credit_risk', 'scorecard']	['classification']
validmind.data_validation.SeasonalDecompose	Seasonal Decompose	Assesses patterns and seasonality in a time series dataset by decomposing its features into foundational components....	['dataset']	{'seasonal_model': {'type': 'str', 'default': 'additive'}}	['time_series_data', 'seasonality', 'statsmodels']	['regression']
validmind.data_validation.ShapiroWilk	Shapiro Wilk	Evaluates feature-wise normality of training data using the Shapiro-Wilk test....	['dataset']	{}	['tabular_data', 'data_distribution', 'statistical_test']	['classification', 'regression']
validmind.data_validation.Skewness	Skewness	Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data...	['dataset']	{'max_threshold': {'type': '_empty', 'default': 1}}	['data_quality', 'tabular_data']	['classification', 'regression']
validmind.data_validation.SpreadPlot	Spread Plot	Assesses potential correlations between pairs of time series variables through visualization to enhance...	['dataset']	{}	['time_series_data', 'visualization']	['regression']
validmind.data_validation.TabularCategoricalBarPlots	Tabular Categorical Bar Plots	Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition....	['dataset']	{}	['tabular_data', 'visualization']	['classification', 'regression']
validmind.data_validation.TabularDateTimeHistograms	Tabular Date Time Histograms	Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime...	['dataset']	{}	['time_series_data', 'visualization']	['classification', 'regression']
validmind.data_validation.TabularDescriptionTables	Tabular Description Tables	Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....	['dataset']	{}	['tabular_data']	['classification', 'regression']
validmind.data_validation.TabularNumericalHistograms	Tabular Numerical Histograms	Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...	['dataset']	{}	['tabular_data', 'visualization']	['classification', 'regression']
validmind.data_validation.TargetRateBarPlots	Target Rate Bar Plots	Generates bar plots visualizing the default rates of categorical features for a classification machine learning...	['dataset']	{}	['tabular_data', 'visualization', 'categorical_data']	['classification']
validmind.data_validation.TimeSeriesDescription	Time Series Description	Generates a detailed analysis for the provided time series dataset, summarizing key statistics to identify trends,...	['dataset']	{}	['time_series_data', 'analysis']	['regression']
validmind.data_validation.TimeSeriesDescriptiveStatistics	Time Series Descriptive Statistics	Evaluates the descriptive statistics of a time series dataset to identify trends, patterns, and data quality issues....	['dataset']	{}	['time_series_data', 'analysis']	['regression']
validmind.data_validation.TimeSeriesFrequency	Time Series Frequency	Evaluates consistency of time series data frequency and generates a frequency plot....	['dataset']	{}	['time_series_data']	['regression']
validmind.data_validation.TimeSeriesHistogram	Time Series Histogram	Visualizes distribution of time-series data using histograms and Kernel Density Estimation (KDE) lines....	['dataset']	{'nbins': {'type': '_empty', 'default': 30}}	['data_validation', 'visualization', 'time_series_data']	['regression', 'time_series_forecasting']
validmind.data_validation.TimeSeriesLinePlot	Time Series Line Plot	Generates and analyses time-series data through line plots revealing trends, patterns, anomalies over time....	['dataset']	{}	['time_series_data', 'visualization']	['regression']
validmind.data_validation.TimeSeriesMissingValues	Time Series Missing Values	Validates time-series data quality by confirming the count of missing values is below a certain threshold....	['dataset']	{'min_threshold': {'type': 'int', 'default': 1}}	['time_series_data']	['regression']
validmind.data_validation.TimeSeriesOutliers	Time Series Outliers	Identifies and visualizes outliers in time-series data using the z-score method....	['dataset']	{'zscore_threshold': {'type': 'int', 'default': 3}}	['time_series_data']	['regression']
validmind.data_validation.TooManyZeroValues	Too Many Zero Values	Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...	['dataset']	{'max_percent_threshold': {'type': 'float', 'default': 0.03}}	['tabular_data']	['regression', 'classification']
validmind.data_validation.UniqueRows	Unique Rows	Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....	['dataset']	{'min_percent_threshold': {'type': 'float', 'default': 1}}	['tabular_data']	['regression', 'classification']
validmind.data_validation.WOEBinPlots	WOE Bin Plots	Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...	['dataset']	{'breaks_adj': {'type': 'list', 'default': None}, 'fig_height': {'type': 'int', 'default': 600}, 'fig_width': {'type': 'int', 'default': 500}}	['tabular_data', 'visualization', 'categorical_data']	['classification']
validmind.data_validation.WOEBinTable	WOE Bin Table	Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power...	['dataset']	{'breaks_adj': {'type': 'list', 'default': None}}	['tabular_data', 'categorical_data']	['classification']
validmind.data_validation.ZivotAndrewsArch	Zivot Andrews Arch	Evaluates the order of integration and stationarity of time series data using the Zivot-Andrews unit root test....	['dataset']	{}	['time_series_data', 'stationarity', 'unit_root_test']	['regression']
validmind.data_validation.nlp.CommonWords	Common Words	Assesses the most frequent non-stopwords in a text column for identifying prevalent language patterns....	['dataset']	{}	['nlp', 'text_data', 'visualization', 'frequency_analysis']	['text_classification', 'text_summarization']
validmind.data_validation.nlp.Hashtags	Hashtags	Assesses hashtag frequency in a text column, highlighting usage trends and potential dataset bias or spam....	['dataset']	{'top_hashtags': {'type': 'int', 'default': 25}}	['nlp', 'text_data', 'visualization', 'frequency_analysis']	['text_classification', 'text_summarization']
validmind.data_validation.nlp.LanguageDetection	Language Detection	Assesses the diversity of languages in a textual dataset by detecting and visualizing the distribution of languages....	['dataset']	{}	['nlp', 'text_data', 'visualization']	['text_classification', 'text_summarization']
validmind.data_validation.nlp.Mentions	Mentions	Calculates and visualizes frequencies of '@' prefixed mentions in a text-based dataset for NLP model analysis....	['dataset']	{'top_mentions': {'type': 'int', 'default': 25}}	['nlp', 'text_data', 'visualization', 'frequency_analysis']	['text_classification', 'text_summarization']
validmind.data_validation.nlp.PolarityAndSubjectivity	Polarity And Subjectivity	Analyzes the polarity and subjectivity of text data within a given dataset to visualize the sentiment distribution....	['dataset']	{'threshold_subjectivity': {'type': '_empty', 'default': 0.5}, 'threshold_polarity': {'type': '_empty', 'default': 0}}	['nlp', 'text_data', 'data_validation']	['nlp']
validmind.data_validation.nlp.Punctuations	Punctuations	Analyzes and visualizes the frequency distribution of punctuation usage in a given text dataset....	['dataset']	{'count_mode': {'type': '_empty', 'default': 'token'}}	['nlp', 'text_data', 'visualization', 'frequency_analysis']	['text_classification', 'text_summarization', 'nlp']
validmind.data_validation.nlp.Sentiment	Sentiment	Analyzes the sentiment of text data within a dataset using the VADER sentiment analysis tool....	['dataset']	{}	['nlp', 'text_data', 'data_validation']	['nlp']
validmind.data_validation.nlp.StopWords	Stop Words	Evaluates and visualizes the frequency of English stop words in a text dataset against a defined threshold....	['dataset']	{'min_percent_threshold': {'type': 'float', 'default': 0.5}, 'num_words': {'type': 'int', 'default': 25}}	['nlp', 'text_data', 'frequency_analysis', 'visualization']	['text_classification', 'text_summarization']
validmind.data_validation.nlp.TextDescription	Text Description	Conducts comprehensive textual analysis on a dataset using NLTK to evaluate various parameters and generate...	['dataset']	{'unwanted_tokens': {'type': 'set', 'default': {"s'", "'s", ' ', 'mr', "''", 'dollar', 'dr', 'mrs', '``', 's', 'us', 'ms'}}, 'lang': {'type': 'str', 'default': 'english'}}	['nlp', 'text_data', 'visualization']	['text_classification', 'text_summarization']
validmind.data_validation.nlp.Toxicity	Toxicity	Assesses the toxicity of text data within a dataset to visualize the distribution of toxicity scores....	['dataset']	{}	['nlp', 'text_data', 'data_validation']	['nlp']
validmind.model_validation.BertScore	Bert Score	Assesses the quality of machine-generated text using BERTScore metrics and visualizes results through histograms...	['dataset', 'model']	{'evaluation_model': {'type': '_empty', 'default': 'distilbert-base-uncased'}}	['nlp', 'text_data', 'visualization']	['text_classification', 'text_summarization']
validmind.model_validation.BleuScore	Bleu Score	Evaluates the quality of machine-generated text using BLEU metrics and visualizes the results through histograms...	['dataset', 'model']	{}	['nlp', 'text_data', 'visualization']	['text_classification', 'text_summarization']
validmind.model_validation.ClusterSizeDistribution	Cluster Size Distribution	Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions...	['dataset', 'model']	{}	['sklearn', 'model_performance']	['clustering']
validmind.model_validation.ContextualRecall	Contextual Recall	Evaluates a Natural Language Generation model's ability to generate contextually relevant and factually correct...	['dataset', 'model']	{}	['nlp', 'text_data', 'visualization']	['text_classification', 'text_summarization']
validmind.model_validation.FeaturesAUC	Features AUC	Evaluates the discriminatory power of each individual feature within a binary classification model by calculating...	['dataset']	{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}	['feature_importance', 'AUC', 'visualization']	['classification']
validmind.model_validation.MeteorScore	Meteor Score	Assesses the quality of machine-generated translations by comparing them to human-produced references using the...	['dataset', 'model']	{}	['nlp', 'text_data', 'visualization']	['text_classification', 'text_summarization']
validmind.model_validation.ModelMetadata	Model Metadata	Compare metadata of different models and generate a summary table with the results....	['model']	{}	['model_training', 'metadata']	['regression', 'time_series_forecasting']
validmind.model_validation.ModelPredictionResiduals	Model Prediction Residuals	Assesses normality and behavior of residuals in regression models through visualization and statistical tests....	['dataset', 'model']	{'nbins': {'type': '_empty', 'default': 100}, 'p_value_threshold': {'type': '_empty', 'default': 0.05}, 'start_date': {'type': '_empty', 'default': None}, 'end_date': {'type': '_empty', 'default': None}}	['regression']	['residual_analysis', 'visualization']
validmind.model_validation.RegardScore	Regard Score	Assesses the sentiment and potential biases in text generated by NLP models by computing and visualizing regard...	['dataset', 'model']	{}	['nlp', 'text_data', 'visualization']	['text_classification', 'text_summarization']
validmind.model_validation.RegressionResidualsPlot	Regression Residuals Plot	Evaluates regression model performance using residual distribution and actual vs. predicted plots....	['model', 'dataset']	{'bin_size': {'type': 'float', 'default': 0.1}}	['model_performance', 'visualization']	['regression']
validmind.model_validation.RougeScore	Rouge Score	Assesses the quality of machine-generated text using ROUGE metrics and visualizes the results to provide...	['dataset', 'model']	{'metric': {'type': '_empty', 'default': 'rouge-1'}}	['nlp', 'text_data', 'visualization']	['text_classification', 'text_summarization']
validmind.model_validation.TimeSeriesPredictionWithCI	Time Series Prediction With CI	Assesses predictive accuracy and uncertainty in time series models, highlighting breaches beyond confidence...	['dataset', 'model']	{'confidence': {'type': '_empty', 'default': 0.95}}	['model_predictions', 'visualization']	['regression', 'time_series_forecasting']
validmind.model_validation.TimeSeriesPredictionsPlot	Time Series Predictions Plot	Plot actual vs predicted values for time series data and generate a visual comparison for the model....	['dataset', 'model']	{}	['model_predictions', 'visualization']	['regression', 'time_series_forecasting']
validmind.model_validation.TimeSeriesR2SquareBySegments	Time Series R2 Square By Segments	Evaluates the R-Squared values of regression models over specified time segments in time series data to assess...	['dataset', 'model']	{'segments': {'type': '_empty', 'default': None}}	['model_performance', 'sklearn']	['regression', 'time_series_forecasting']
validmind.model_validation.TokenDisparity	Token Disparity	Evaluates the token disparity between reference and generated texts, visualizing the results through histograms and...	['dataset', 'model']	{}	['nlp', 'text_data', 'visualization']	['text_classification', 'text_summarization']
validmind.model_validation.ToxicityScore	Toxicity Score	Assesses the toxicity levels of texts generated by NLP models to identify and mitigate harmful or offensive content....	['dataset', 'model']	{}	['nlp', 'text_data', 'visualization']	['text_classification', 'text_summarization']
validmind.model_validation.embeddings.ClusterDistribution	Cluster Distribution	Assesses the distribution of text embeddings across clusters produced by a model using KMeans clustering....	['model', 'dataset']	{'num_clusters': {'type': 'int', 'default': 5}}	['llm', 'text_data', 'embeddings', 'visualization']	['feature_extraction']
validmind.model_validation.embeddings.CosineSimilarityComparison	Cosine Similarity Comparison	Assesses the similarity between embeddings generated by different models using Cosine Similarity, providing both...	['dataset', 'models']	{}	['visualization', 'dimensionality_reduction', 'embeddings']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.embeddings.CosineSimilarityDistribution	Cosine Similarity Distribution	Assesses the similarity between predicted text embeddings from a model using a Cosine Similarity distribution...	['dataset', 'model']	{}	['llm', 'text_data', 'embeddings', 'visualization']	['feature_extraction']
validmind.model_validation.embeddings.CosineSimilarityHeatmap	Cosine Similarity Heatmap	Generates an interactive heatmap to visualize the cosine similarities among embeddings derived from a given model....	['dataset', 'model']	{'title': {'type': '_empty', 'default': 'Cosine Similarity Matrix'}, 'color': {'type': '_empty', 'default': 'Cosine Similarity'}, 'xaxis_title': {'type': '_empty', 'default': 'Index'}, 'yaxis_title': {'type': '_empty', 'default': 'Index'}, 'color_scale': {'type': '_empty', 'default': 'Blues'}}	['visualization', 'dimensionality_reduction', 'embeddings']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.embeddings.DescriptiveAnalytics	Descriptive Analytics	Evaluates statistical properties of text embeddings in an ML model via mean, median, and standard deviation...	['dataset', 'model']	{}	['llm', 'text_data', 'embeddings', 'visualization']	['feature_extraction']
validmind.model_validation.embeddings.EmbeddingsVisualization2D	Embeddings Visualization2 D	Visualizes 2D representation of text embeddings generated by a model using t-SNE technique....	['model', 'dataset']	{'cluster_column': {'type': None, 'default': None}, 'perplexity': {'type': 'int', 'default': 30}}	['llm', 'text_data', 'embeddings', 'visualization']	['feature_extraction']
validmind.model_validation.embeddings.EuclideanDistanceComparison	Euclidean Distance Comparison	Assesses and visualizes the dissimilarity between model embeddings using Euclidean distance, providing insights...	['dataset', 'models']	{}	['visualization', 'dimensionality_reduction', 'embeddings']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.embeddings.EuclideanDistanceHeatmap	Euclidean Distance Heatmap	Generates an interactive heatmap to visualize the Euclidean distances among embeddings derived from a given model....	['dataset', 'model']	{'title': {'type': '_empty', 'default': 'Euclidean Distance Matrix'}, 'color': {'type': '_empty', 'default': 'Euclidean Distance'}, 'xaxis_title': {'type': '_empty', 'default': 'Index'}, 'yaxis_title': {'type': '_empty', 'default': 'Index'}, 'color_scale': {'type': '_empty', 'default': 'Blues'}}	['visualization', 'dimensionality_reduction', 'embeddings']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.embeddings.PCAComponentsPairwisePlots	PCA Components Pairwise Plots	Generates scatter plots for pairwise combinations of principal component analysis (PCA) components of model...	['dataset', 'model']	{'n_components': {'type': '_empty', 'default': 3}}	['visualization', 'dimensionality_reduction', 'embeddings']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.embeddings.StabilityAnalysisKeyword	Stability Analysis Keyword	Evaluates robustness of embedding models to keyword swaps in the test dataset....	['dataset', 'model']	{'keyword_dict': {'type': None, 'default': None}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}	['llm', 'text_data', 'embeddings', 'visualization']	['feature_extraction']
validmind.model_validation.embeddings.StabilityAnalysisRandomNoise	Stability Analysis Random Noise	Assesses the robustness of text embeddings models to random noise introduced via text perturbations....	['dataset', 'model']	{'probability': {'type': 'float', 'default': 0.02}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}	['llm', 'text_data', 'embeddings', 'visualization']	['feature_extraction']
validmind.model_validation.embeddings.StabilityAnalysisSynonyms	Stability Analysis Synonyms	Evaluates the stability of text embeddings models when words in test data are replaced by their synonyms randomly....	['dataset', 'model']	{'probability': {'type': 'float', 'default': 0.02}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}	['llm', 'text_data', 'embeddings', 'visualization']	['feature_extraction']
validmind.model_validation.embeddings.StabilityAnalysisTranslation	Stability Analysis Translation	Evaluates robustness of text embeddings models to noise introduced by translating the original text to another...	['dataset', 'model']	{'source_lang': {'type': 'str', 'default': 'en'}, 'target_lang': {'type': 'str', 'default': 'fr'}, 'mean_similarity_threshold': {'type': 'float', 'default': 0.7}}	['llm', 'text_data', 'embeddings', 'visualization']	['feature_extraction']
validmind.model_validation.embeddings.TSNEComponentsPairwisePlots	TSNE Components Pairwise Plots	Creates scatter plots for pairwise combinations of t-SNE components to visualize embeddings and highlight potential...	['dataset', 'model']	{'n_components': {'type': '_empty', 'default': 2}, 'perplexity': {'type': '_empty', 'default': 30}, 'title': {'type': '_empty', 'default': 't-SNE'}}	['visualization', 'dimensionality_reduction', 'embeddings']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.ragas.AnswerCorrectness	Answer Correctness	Evaluates the correctness of answers in a dataset with respect to the provided ground...	['dataset']	{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'response_column': {'type': '_empty', 'default': 'response'}, 'reference_column': {'type': '_empty', 'default': 'reference'}}	['ragas', 'llm']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.ragas.AspectCritic	Aspect Critic	Evaluates generations against the following aspects: harmfulness, maliciousness,...	['dataset']	{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'response_column': {'type': '_empty', 'default': 'response'}, 'retrieved_contexts_column': {'type': '_empty', 'default': None}, 'aspects': {'type': 'list', 'default': ['coherence', 'conciseness', 'correctness', 'harmfulness', 'maliciousness']}, 'additional_aspects': {'type': 'list', 'default': None}}	['ragas', 'llm', 'qualitative']	['text_summarization', 'text_generation', 'text_qa']
validmind.model_validation.ragas.ContextEntityRecall	Context Entity Recall	Evaluates the context entity recall for dataset entries and visualizes the results....	['dataset']	{'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}}	['ragas', 'llm', 'retrieval_performance']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.ragas.ContextPrecision	Context Precision	Context Precision is a metric that evaluates whether all of the ground-truth...	['dataset']	{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}}	['ragas', 'llm', 'retrieval_performance']	['text_qa', 'text_generation', 'text_summarization', 'text_classification']
validmind.model_validation.ragas.ContextPrecisionWithoutReference	Context Precision Without Reference	Context Precision Without Reference is a metric used to evaluate the relevance of...	['dataset']	{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'response_column': {'type': 'str', 'default': 'response'}}	['ragas', 'llm', 'retrieval_performance']	['text_qa', 'text_generation', 'text_summarization', 'text_classification']
validmind.model_validation.ragas.ContextRecall	Context Recall	Context recall measures the extent to which the retrieved context aligns with the...	['dataset']	{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}}	['ragas', 'llm', 'retrieval_performance']	['text_qa', 'text_generation', 'text_summarization', 'text_classification']
validmind.model_validation.ragas.Faithfulness	Faithfulness	Evaluates the faithfulness of the generated answers with respect to retrieved contexts....	['dataset']	{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'response_column': {'type': '_empty', 'default': 'response'}, 'retrieved_contexts_column': {'type': '_empty', 'default': 'retrieved_contexts'}}	['ragas', 'llm', 'rag_performance']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.ragas.NoiseSensitivity	Noise Sensitivity	Assesses the sensitivity of a Large Language Model (LLM) to noise in retrieved context by measuring how often it...	['dataset']	{'response_column': {'type': '_empty', 'default': 'response'}, 'retrieved_contexts_column': {'type': '_empty', 'default': 'retrieved_contexts'}, 'reference_column': {'type': '_empty', 'default': 'reference'}, 'focus': {'type': '_empty', 'default': 'relevant'}, 'user_input_column': {'type': '_empty', 'default': 'user_input'}}	['ragas', 'llm', 'rag_performance']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.ragas.ResponseRelevancy	Response Relevancy	Assesses how pertinent the generated answer is to the given prompt....	['dataset']	{'user_input_column': {'type': '_empty', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': '_empty', 'default': None}, 'response_column': {'type': '_empty', 'default': 'response'}}	['ragas', 'llm', 'rag_performance']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.ragas.SemanticSimilarity	Semantic Similarity	Calculates the semantic similarity between generated responses and ground truths...	['dataset']	{'response_column': {'type': '_empty', 'default': 'response'}, 'reference_column': {'type': '_empty', 'default': 'reference'}}	['ragas', 'llm']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.sklearn.AdjustedMutualInformation	Adjusted Mutual Information	Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...	['model', 'dataset']	{}	['sklearn', 'model_performance', 'clustering']	['clustering']
validmind.model_validation.sklearn.AdjustedRandIndex	Adjusted Rand Index	Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...	['model', 'dataset']	{}	['sklearn', 'model_performance', 'clustering']	['clustering']
validmind.model_validation.sklearn.CalibrationCurve	Calibration Curve	Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...	['model', 'dataset']	{'n_bins': {'type': 'int', 'default': 10}}	['sklearn', 'model_performance', 'classification']	['classification']
validmind.model_validation.sklearn.ClassifierPerformance	Classifier Performance	Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...	['dataset', 'model']	{'average': {'type': 'str', 'default': 'macro'}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.ClassifierThresholdOptimization	Classifier Threshold Optimization	Analyzes and visualizes different threshold optimization methods for binary classification models....	['dataset', 'model']	{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}	['model_validation', 'threshold_optimization', 'classification_metrics']	['classification']
validmind.model_validation.sklearn.ClusterCosineSimilarity	Cluster Cosine Similarity	Measures the intra-cluster similarity of a clustering model using cosine similarity....	['model', 'dataset']	{}	['sklearn', 'model_performance', 'clustering']	['clustering']
validmind.model_validation.sklearn.ClusterPerformanceMetrics	Cluster Performance Metrics	Evaluates the performance of clustering machine learning models using multiple established metrics....	['model', 'dataset']	{}	['sklearn', 'model_performance', 'clustering']	['clustering']
validmind.model_validation.sklearn.CompletenessScore	Completeness Score	Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....	['model', 'dataset']	{}	['sklearn', 'model_performance', 'clustering']	['clustering']
validmind.model_validation.sklearn.ConfusionMatrix	Confusion Matrix	Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...	['dataset', 'model']	{'threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.FeatureImportance	Feature Importance	Compute feature importance scores for a given model and generate a summary table...	['dataset', 'model']	{'num_features': {'type': 'int', 'default': 3}}	['model_explainability', 'sklearn']	['regression', 'time_series_forecasting']
validmind.model_validation.sklearn.FowlkesMallowsScore	Fowlkes Mallows Score	Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...	['dataset', 'model']	{}	['sklearn', 'model_performance']	['clustering']
validmind.model_validation.sklearn.HomogeneityScore	Homogeneity Score	Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...	['dataset', 'model']	{}	['sklearn', 'model_performance']	['clustering']
validmind.model_validation.sklearn.HyperParametersTuning	Hyper Parameters Tuning	Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...	['model', 'dataset']	{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}	['sklearn', 'model_performance']	['clustering', 'classification']
validmind.model_validation.sklearn.KMeansClustersOptimization	K Means Clusters Optimization	Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....	['model', 'dataset']	{'n_clusters': {'type': None, 'default': None}}	['sklearn', 'model_performance', 'kmeans']	['clustering']
validmind.model_validation.sklearn.MinimumAccuracy	Minimum Accuracy	Checks if the model's prediction accuracy meets or surpasses a specified threshold....	['dataset', 'model']	{'min_threshold': {'type': 'float', 'default': 0.7}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.MinimumF1Score	Minimum F1 Score	Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...	['dataset', 'model']	{'min_threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.MinimumROCAUCScore	Minimum ROCAUC Score	Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....	['dataset', 'model']	{'min_threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.ModelParameters	Model Parameters	Extracts and displays model parameters in a structured format for transparency and reproducibility....	['model']	{'model_params': {'type': '_empty', 'default': None}}	['model_training', 'metadata']	['classification', 'regression']
validmind.model_validation.sklearn.ModelsPerformanceComparison	Models Performance Comparison	Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...	['dataset', 'models']	{}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']	['classification', 'text_classification']
validmind.model_validation.sklearn.OverfitDiagnosis	Overfit Diagnosis	Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...	['model', 'datasets']	{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}	['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']	['classification', 'regression']
validmind.model_validation.sklearn.PermutationFeatureImportance	Permutation Feature Importance	Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...	['model', 'dataset']	{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}	['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.PopulationStabilityIndex	Population Stability Index	Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...	['datasets', 'model']	{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.PrecisionRecallCurve	Precision Recall Curve	Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....	['model', 'dataset']	{}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.ROCCurve	ROC Curve	Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...	['model', 'dataset']	{}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.RegressionErrors	Regression Errors	Assesses the performance and error distribution of a regression model using various error metrics....	['model', 'dataset']	{}	['sklearn', 'model_performance']	['regression', 'classification']
validmind.model_validation.sklearn.RegressionErrorsComparison	Regression Errors Comparison	Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing...	['datasets', 'models']	{}	['model_performance', 'sklearn']	['regression', 'time_series_forecasting']
validmind.model_validation.sklearn.RegressionPerformance	Regression Performance	Evaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD....	['model', 'dataset']	{}	['sklearn', 'model_performance']	['regression']
validmind.model_validation.sklearn.RegressionR2Square	Regression R2 Square	Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj...	['dataset', 'model']	{}	['sklearn', 'model_performance']	['regression']
validmind.model_validation.sklearn.RegressionR2SquareComparison	Regression R2 Square Comparison	Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess...	['datasets', 'models']	{}	['model_performance', 'sklearn']	['regression', 'time_series_forecasting']
validmind.model_validation.sklearn.RobustnessDiagnosis	Robustness Diagnosis	Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....	['datasets', 'model']	{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}	['sklearn', 'model_diagnosis', 'visualization']	['classification', 'regression']
validmind.model_validation.sklearn.SHAPGlobalImportance	SHAP Global Importance	Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....	['model', 'dataset']	{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}	['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.ScoreProbabilityAlignment	Score Probability Alignment	Analyzes the alignment between credit scores and predicted probabilities....	['model', 'dataset']	{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}	['visualization', 'credit_risk', 'calibration']	['classification']
validmind.model_validation.sklearn.SilhouettePlot	Silhouette Plot	Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML...	['model', 'dataset']	{}	['sklearn', 'model_performance']	['clustering']
validmind.model_validation.sklearn.TrainingTestDegradation	Training Test Degradation	Tests if model performance degradation between training and test datasets exceeds a predefined threshold....	['datasets', 'model']	{'max_threshold': {'type': 'float', 'default': 0.1}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.VMeasure	V Measure	Evaluates homogeneity and completeness of a clustering model using the V Measure Score....	['dataset', 'model']	{}	['sklearn', 'model_performance']	['clustering']
validmind.model_validation.sklearn.WeakspotsDiagnosis	Weakspots Diagnosis	Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...	['datasets', 'model']	{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']	['classification', 'text_classification']
validmind.model_validation.statsmodels.AutoARIMA	Auto ARIMA	Evaluates ARIMA models for time-series forecasting, ranking them using Bayesian and Akaike Information Criteria....	['model', 'dataset']	{}	['time_series_data', 'forecasting', 'model_selection', 'statsmodels']	['regression']
validmind.model_validation.statsmodels.CumulativePredictionProbabilities	Cumulative Prediction Probabilities	Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models....	['dataset', 'model']	{'title': {'type': '_empty', 'default': 'Cumulative Probabilities'}}	['visualization', 'credit_risk']	['classification']
validmind.model_validation.statsmodels.DurbinWatsonTest	Durbin Watson Test	Assesses autocorrelation in time series data features using the Durbin-Watson statistic....	['dataset', 'model']	{'threshold': {'type': '_empty', 'default': [1.5, 2.5]}}	['time_series_data', 'forecasting', 'statistical_test', 'statsmodels']	['regression']
validmind.model_validation.statsmodels.GINITable	GINI Table	Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....	['dataset', 'model']	{}	['model_performance']	['classification']
validmind.model_validation.statsmodels.KolmogorovSmirnov	Kolmogorov Smirnov	Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test....	['model', 'dataset']	{'dist': {'type': 'str', 'default': 'norm'}}	['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']	['classification', 'regression']
validmind.model_validation.statsmodels.Lilliefors	Lilliefors	Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....	['dataset']	{}	['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']	['classification', 'regression']
validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram	Prediction Probabilities Histogram	Assesses the predictive probability distribution for binary classification to evaluate model performance and...	['dataset', 'model']	{'title': {'type': '_empty', 'default': 'Histogram of Predictive Probabilities'}}	['visualization', 'credit_risk']	['classification']
validmind.model_validation.statsmodels.RegressionCoeffs	Regression Coeffs	Assesses the significance and uncertainty of predictor variables in a regression model through visualization of...	['model']	{}	['tabular_data', 'visualization', 'model_training']	['regression']
validmind.model_validation.statsmodels.RegressionFeatureSignificance	Regression Feature Significance	Assesses and visualizes the statistical significance of features in a regression model....	['model']	{'fontsize': {'type': 'int', 'default': 10}, 'p_threshold': {'type': 'float', 'default': 0.05}}	['statistical_test', 'model_interpretation', 'visualization', 'feature_importance']	['regression']
validmind.model_validation.statsmodels.RegressionModelForecastPlot	Regression Model Forecast Plot	Generates plots to visually compare the forecasted outcomes of a regression model against actual observed values over...	['model', 'dataset']	{'start_date': {'type': None, 'default': None}, 'end_date': {'type': None, 'default': None}}	['time_series_data', 'forecasting', 'visualization']	['regression']
validmind.model_validation.statsmodels.RegressionModelForecastPlotLevels	Regression Model Forecast Plot Levels	Assesses the alignment between forecasted and observed values in regression models through visual plots...	['model', 'dataset']	{}	['time_series_data', 'forecasting', 'visualization']	['regression']
validmind.model_validation.statsmodels.RegressionModelSensitivityPlot	Regression Model Sensitivity Plot	Assesses the sensitivity of a regression model to changes in independent variables by applying shocks and...	['dataset', 'model']	{'shocks': {'type': None, 'default': [0.1]}, 'transformation': {'type': None, 'default': None}}	['senstivity_analysis', 'visualization']	['regression']
validmind.model_validation.statsmodels.RegressionModelSummary	Regression Model Summary	Evaluates regression model performance using metrics including R-Squared, Adjusted R-Squared, MSE, and RMSE....	['dataset', 'model']	{}	['model_performance', 'regression']	['regression']
validmind.model_validation.statsmodels.RegressionPermutationFeatureImportance	Regression Permutation Feature Importance	Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...	['dataset', 'model']	{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}	['statsmodels', 'feature_importance', 'visualization']	['regression']
validmind.model_validation.statsmodels.ScorecardHistogram	Scorecard Histogram	The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,...	['dataset']	{'title': {'type': '_empty', 'default': 'Histogram of Scores'}, 'score_column': {'type': '_empty', 'default': 'score'}}	['visualization', 'credit_risk', 'logistic_regression']	['classification']
validmind.ongoing_monitoring.CalibrationCurveDrift	Calibration Curve Drift	Evaluates changes in probability calibration between reference and monitoring datasets....	['datasets', 'model']	{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.ongoing_monitoring.ClassDiscriminationDrift	Class Discrimination Drift	Compares classification discrimination metrics between reference and monitoring datasets....	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.ongoing_monitoring.ClassImbalanceDrift	Class Imbalance Drift	Evaluates drift in class distribution between reference and monitoring datasets....	['datasets']	{'drift_pct_threshold': {'type': 'float', 'default': 5.0}, 'title': {'type': 'str', 'default': 'Class Distribution Drift'}}	['tabular_data', 'binary_classification', 'multiclass_classification']	['classification']
validmind.ongoing_monitoring.ClassificationAccuracyDrift	Classification Accuracy Drift	Compares classification accuracy metrics between reference and monitoring datasets....	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.ongoing_monitoring.ConfusionMatrixDrift	Confusion Matrix Drift	Compares confusion matrix metrics between reference and monitoring datasets....	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift	Cumulative Prediction Probabilities Drift	Compares cumulative prediction probability distributions between reference and monitoring datasets....	['datasets', 'model']	{}	['visualization', 'credit_risk']	['classification']
validmind.ongoing_monitoring.FeatureDrift	Feature Drift	Evaluates changes in feature distribution over time to identify potential model drift....	['datasets']	{'bins': {'type': '_empty', 'default': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}, 'feature_columns': {'type': '_empty', 'default': None}, 'psi_threshold': {'type': '_empty', 'default': 0.2}}	['visualization']	['monitoring']
validmind.ongoing_monitoring.PredictionAcrossEachFeature	Prediction Across Each Feature	Assesses differences in model predictions across individual features between reference and monitoring datasets...	['datasets', 'model']	{}	['visualization']	['monitoring']
validmind.ongoing_monitoring.PredictionCorrelation	Prediction Correlation	Assesses correlation changes between model predictions from reference and monitoring datasets to detect potential...	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['visualization']	['monitoring']
validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift	Prediction Probabilities Histogram Drift	Compares prediction probability distributions between reference and monitoring datasets....	['datasets', 'model']	{'title': {'type': '_empty', 'default': 'Prediction Probabilities Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}	['visualization', 'credit_risk']	['classification']
validmind.ongoing_monitoring.PredictionQuantilesAcrossFeatures	Prediction Quantiles Across Features	Assesses differences in model prediction distributions across individual features between reference...	['datasets', 'model']	{}	['visualization']	['monitoring']
validmind.ongoing_monitoring.ROCCurveDrift	ROC Curve Drift	Compares ROC curves between reference and monitoring datasets....	['datasets', 'model']	{}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.ongoing_monitoring.ScoreBandsDrift	Score Bands Drift	Analyzes drift in population distribution and default rates across score bands....	['datasets', 'model']	{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}, 'drift_threshold': {'type': 'float', 'default': 20.0}}	['visualization', 'credit_risk', 'scorecard']	['classification']
validmind.ongoing_monitoring.ScorecardHistogramDrift	Scorecard Histogram Drift	Compares score distributions between reference and monitoring datasets for each class....	['datasets']	{'score_column': {'type': 'str', 'default': 'score'}, 'title': {'type': 'str', 'default': 'Scorecard Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}	['visualization', 'credit_risk', 'logistic_regression']	['classification']
validmind.ongoing_monitoring.TargetPredictionDistributionPlot	Target Prediction Distribution Plot	Assesses differences in prediction distributions between a reference dataset and a monitoring dataset to identify...	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['visualization']	['monitoring']
validmind.prompt_validation.Bias	Bias	Assesses potential bias in a Large Language Model by analyzing the distribution and order of exemplars in the...	['model']	{'min_threshold': {'type': '_empty', 'default': 7}}	['llm', 'few_shot']	['text_classification', 'text_summarization']
validmind.prompt_validation.Clarity	Clarity	Evaluates and scores the clarity of prompts in a Large Language Model based on specified guidelines....	['model']	{'min_threshold': {'type': '_empty', 'default': 7}}	['llm', 'zero_shot', 'few_shot']	['text_classification', 'text_summarization']
validmind.prompt_validation.Conciseness	Conciseness	Analyzes and grades the conciseness of prompts provided to a Large Language Model....	['model']	{'min_threshold': {'type': '_empty', 'default': 7}}	['llm', 'zero_shot', 'few_shot']	['text_classification', 'text_summarization']
validmind.prompt_validation.Delimitation	Delimitation	Evaluates the proper use of delimiters in prompts provided to Large Language Models....	['model']	{'min_threshold': {'type': '_empty', 'default': 7}}	['llm', 'zero_shot', 'few_shot']	['text_classification', 'text_summarization']
validmind.prompt_validation.NegativeInstruction	Negative Instruction	Evaluates and grades the use of affirmative, proactive language over negative instructions in LLM prompts....	['model']	{'min_threshold': {'type': '_empty', 'default': 7}}	['llm', 'zero_shot', 'few_shot']	['text_classification', 'text_summarization']
validmind.prompt_validation.Robustness	Robustness	Assesses the robustness of prompts provided to a Large Language Model under varying conditions and contexts. This test...	['model', 'dataset']	{'num_tests': {'type': '_empty', 'default': 10}}	['llm', 'zero_shot', 'few_shot']	['text_classification', 'text_summarization']
validmind.prompt_validation.Specificity	Specificity	Evaluates and scores the specificity of prompts provided to a Large Language Model (LLM), based on clarity, detail,...	['model']	{'min_threshold': {'type': '_empty', 'default': 7}}	['llm', 'zero_shot', 'few_shot']	['text_classification', 'text_summarization']
validmind.unit_metrics.classification.Accuracy	Accuracy	Calculates the accuracy of a model	['dataset', 'model']	{}	['classification']	['classification']
validmind.unit_metrics.classification.F1	F1	Calculates the F1 score for a classification model.	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.Precision	Precision	Calculates the precision for a classification model.	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.ROC_AUC	ROC AUC	Calculates the ROC AUC for a classification model.	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.Recall	Recall	Calculates the recall for a classification model.	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.regression.AdjustedRSquaredScore	Adjusted R Squared Score	Calculates the adjusted R-squared score for a regression model.	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.GiniCoefficient	Gini Coefficient	Calculates the Gini coefficient for a regression model.	['dataset', 'model']	{}	['regression']	['regression']
validmind.unit_metrics.regression.HuberLoss	Huber Loss	Calculates the Huber loss for a regression model.	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.KolmogorovSmirnovStatistic	Kolmogorov Smirnov Statistic	Calculates the Kolmogorov-Smirnov statistic for a regression model.	['dataset', 'model']	{}	['regression']	['regression']
validmind.unit_metrics.regression.MeanAbsoluteError	Mean Absolute Error	Calculates the mean absolute error for a regression model.	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.MeanAbsolutePercentageError	Mean Absolute Percentage Error	Calculates the mean absolute percentage error for a regression model.	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.MeanBiasDeviation	Mean Bias Deviation	Calculates the mean bias deviation for a regression model.	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.MeanSquaredError	Mean Squared Error	Calculates the mean squared error for a regression model.	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.QuantileLoss	Quantile Loss	Calculates the quantile loss for a regression model.	['model', 'dataset']	{'quantile': {'type': '_empty', 'default': 0.5}}	['regression']	['regression']
validmind.unit_metrics.regression.RSquaredScore	R Squared Score	Calculates the R-squared score for a regression model.	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.RootMeanSquaredError	Root Mean Squared Error	Calculates the root mean squared error for a regression model.	['model', 'dataset']	{}	['regression']	['regression']

Understanding Tags and Task Types

Effectively using ValidMind's tests involves a deep understanding of its 'tags' and 'task types'. Here's a breakdown:

Task Types: Represent the kind of modeling task associated with a test. For instance:
- classification: Works with Classification Models and Datasets
- regression: Works with Regression Models and Datasets
- text classification: Works with Text Classification Models and Datasets
- text summarization: Works with Text Summarization Models and Datasets
Tags: Free-form descriptors providing more details about the test, what data and models the test is compatible with and what category the test falls into etc. Some examples include:
- llm: Tests that work with Large Language Models
- nlp: Tests relevant for natural language processing.
- binary_classification: Tests for binary classification tasks.
- forecasting: Tests for forecasting and time-series analysis.
- tabular_data: Tests for tabular data like CSVs and Excel spreadsheets.

You can use the functions list_tasks() and list_tags() to view all the tasks and tags used for classifying all the tests available in the ValidMind Library:

list_tasks()

['time_series_forecasting',
 'feature_extraction',
 'text_qa',
 'text_generation',
 'residual_analysis',
 'visualization',
 'text_classification',
 'regression',
 'nlp',
 'text_summarization',
 'data_validation',
 'classification',
 'clustering',
 'monitoring']

list_tags()

['few_shot',
 'ragas',
 'bias_and_fairness',
 'AUC',
 'visualization',
 'rag_performance',
 'logistic_regression',
 'model_validation',
 'credit_risk',
 'model_selection',
 'linear_regression',
 'clustering',
 'data_distribution',
 'model_explainability',
 'frequency_analysis',
 'model_interpretation',
 'time_series_data',
 'forecasting',
 'llm',
 'multiclass_classification',
 'data_validation',
 'binary_classification',
 'stationarity',
 'senstivity_analysis',
 'retrieval_performance',
 'categorical_data',
 'seasonality',
 'qualitative',
 'model_comparison',
 'model_training',
 'data_quality',
 'regression',
 'anomaly_detection',
 'calibration',
 'model_predictions',
 'dimensionality_reduction',
 'descriptive_statistics',
 'classification',
 'unit_root_test',
 'metadata',
 'threshold_optimization',
 'model_diagnosis',
 'feature_selection',
 'data_analysis',
 'statistical_test',
 'embeddings',
 'analysis',
 'feature_importance',
 'scorecard',
 'correlation',
 'classification_metrics',
 'nlp',
 'sklearn',
 'kmeans',
 'statsmodels',
 'numerical_data',
 'zero_shot',
 'text_data',
 'tabular_data',
 'model_performance']

If you want to see which tags correspond to which task type, you can use the function list_tasks_and_tags():

list_tasks_and_tags()

Task	Tags
regression	bias_and_fairness, visualization, model_selection, linear_regression, data_distribution, model_explainability, model_interpretation, time_series_data, forecasting, multiclass_classification, data_validation, binary_classification, stationarity, model_performance, senstivity_analysis, categorical_data, seasonality, data_quality, regression, model_predictions, descriptive_statistics, unit_root_test, metadata, model_diagnosis, feature_selection, data_analysis, statistical_test, analysis, feature_importance, correlation, sklearn, statsmodels, numerical_data, text_data, tabular_data, model_training
classification	bias_and_fairness, AUC, visualization, logistic_regression, model_validation, credit_risk, linear_regression, data_distribution, time_series_data, multiclass_classification, binary_classification, categorical_data, model_comparison, model_training, data_quality, anomaly_detection, calibration, descriptive_statistics, classification, metadata, model_diagnosis, threshold_optimization, feature_selection, data_analysis, statistical_test, classification_metrics, feature_importance, scorecard, correlation, sklearn, statsmodels, numerical_data, text_data, tabular_data, model_performance
text_classification	few_shot, ragas, visualization, frequency_analysis, model_comparison, feature_importance, time_series_data, nlp, llm, sklearn, multiclass_classification, zero_shot, text_data, binary_classification, retrieval_performance, tabular_data, model_performance, model_diagnosis
text_summarization	few_shot, ragas, qualitative, visualization, frequency_analysis, embeddings, rag_performance, time_series_data, nlp, llm, zero_shot, text_data, dimensionality_reduction, retrieval_performance, tabular_data
data_validation	stationarity, time_series_data, statsmodels, unit_root_test
time_series_forecasting	model_explainability, visualization, time_series_data, sklearn, model_predictions, data_validation, model_performance, model_training, metadata
nlp	visualization, frequency_analysis, data_validation, nlp, text_data
clustering	sklearn, kmeans, clustering, model_performance
residual_analysis	regression
visualization	regression
feature_extraction	text_data, llm, visualization, embeddings
text_qa	ragas, qualitative, visualization, embeddings, rag_performance, llm, dimensionality_reduction, retrieval_performance
text_generation	ragas, qualitative, visualization, embeddings, rag_performance, llm, dimensionality_reduction, retrieval_performance
monitoring	visualization

Searching for Specific Tests using `tags` and `tasks`

While listing all tests is valuable, there are times when you need to narrow down your search. The list_tests function offers filter, task, and tags parameters to assist in this.

If you're targeting a specific test or tests that match a particular task type, the filter parameter comes in handy. For example, to list tests that are compatible with 'sklearn' models:

list_tests(filter="sklearn")

ID	Name	Description	Required Inputs	Params	Tags	Tasks
validmind.model_validation.ClusterSizeDistribution	Cluster Size Distribution	Assesses the performance of clustering models by comparing the distribution of cluster sizes in model predictions...	['dataset', 'model']	{}	['sklearn', 'model_performance']	['clustering']
validmind.model_validation.TimeSeriesR2SquareBySegments	Time Series R2 Square By Segments	Evaluates the R-Squared values of regression models over specified time segments in time series data to assess...	['dataset', 'model']	{'segments': {'type': '_empty', 'default': None}}	['model_performance', 'sklearn']	['regression', 'time_series_forecasting']
validmind.model_validation.sklearn.AdjustedMutualInformation	Adjusted Mutual Information	Evaluates clustering model performance by measuring mutual information between true and predicted labels, adjusting...	['model', 'dataset']	{}	['sklearn', 'model_performance', 'clustering']	['clustering']
validmind.model_validation.sklearn.AdjustedRandIndex	Adjusted Rand Index	Measures the similarity between two data clusters using the Adjusted Rand Index (ARI) metric in clustering machine...	['model', 'dataset']	{}	['sklearn', 'model_performance', 'clustering']	['clustering']
validmind.model_validation.sklearn.CalibrationCurve	Calibration Curve	Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...	['model', 'dataset']	{'n_bins': {'type': 'int', 'default': 10}}	['sklearn', 'model_performance', 'classification']	['classification']
validmind.model_validation.sklearn.ClassifierPerformance	Classifier Performance	Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...	['dataset', 'model']	{'average': {'type': 'str', 'default': 'macro'}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.ClassifierThresholdOptimization	Classifier Threshold Optimization	Analyzes and visualizes different threshold optimization methods for binary classification models....	['dataset', 'model']	{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}	['model_validation', 'threshold_optimization', 'classification_metrics']	['classification']
validmind.model_validation.sklearn.ClusterCosineSimilarity	Cluster Cosine Similarity	Measures the intra-cluster similarity of a clustering model using cosine similarity....	['model', 'dataset']	{}	['sklearn', 'model_performance', 'clustering']	['clustering']
validmind.model_validation.sklearn.ClusterPerformanceMetrics	Cluster Performance Metrics	Evaluates the performance of clustering machine learning models using multiple established metrics....	['model', 'dataset']	{}	['sklearn', 'model_performance', 'clustering']	['clustering']
validmind.model_validation.sklearn.CompletenessScore	Completeness Score	Evaluates a clustering model's capacity to categorize instances from a single class into the same cluster....	['model', 'dataset']	{}	['sklearn', 'model_performance', 'clustering']	['clustering']
validmind.model_validation.sklearn.ConfusionMatrix	Confusion Matrix	Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...	['dataset', 'model']	{'threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.FeatureImportance	Feature Importance	Compute feature importance scores for a given model and generate a summary table...	['dataset', 'model']	{'num_features': {'type': 'int', 'default': 3}}	['model_explainability', 'sklearn']	['regression', 'time_series_forecasting']
validmind.model_validation.sklearn.FowlkesMallowsScore	Fowlkes Mallows Score	Evaluates the similarity between predicted and actual cluster assignments in a model using the Fowlkes-Mallows...	['dataset', 'model']	{}	['sklearn', 'model_performance']	['clustering']
validmind.model_validation.sklearn.HomogeneityScore	Homogeneity Score	Assesses clustering homogeneity by comparing true and predicted labels, scoring from 0 (heterogeneous) to 1...	['dataset', 'model']	{}	['sklearn', 'model_performance']	['clustering']
validmind.model_validation.sklearn.HyperParametersTuning	Hyper Parameters Tuning	Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...	['model', 'dataset']	{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}	['sklearn', 'model_performance']	['clustering', 'classification']
validmind.model_validation.sklearn.KMeansClustersOptimization	K Means Clusters Optimization	Optimizes the number of clusters in K-means models using Elbow and Silhouette methods....	['model', 'dataset']	{'n_clusters': {'type': None, 'default': None}}	['sklearn', 'model_performance', 'kmeans']	['clustering']
validmind.model_validation.sklearn.MinimumAccuracy	Minimum Accuracy	Checks if the model's prediction accuracy meets or surpasses a specified threshold....	['dataset', 'model']	{'min_threshold': {'type': 'float', 'default': 0.7}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.MinimumF1Score	Minimum F1 Score	Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...	['dataset', 'model']	{'min_threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.MinimumROCAUCScore	Minimum ROCAUC Score	Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....	['dataset', 'model']	{'min_threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.ModelParameters	Model Parameters	Extracts and displays model parameters in a structured format for transparency and reproducibility....	['model']	{'model_params': {'type': '_empty', 'default': None}}	['model_training', 'metadata']	['classification', 'regression']
validmind.model_validation.sklearn.ModelsPerformanceComparison	Models Performance Comparison	Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...	['dataset', 'models']	{}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']	['classification', 'text_classification']
validmind.model_validation.sklearn.OverfitDiagnosis	Overfit Diagnosis	Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...	['model', 'datasets']	{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}	['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']	['classification', 'regression']
validmind.model_validation.sklearn.PermutationFeatureImportance	Permutation Feature Importance	Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...	['model', 'dataset']	{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}	['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.PopulationStabilityIndex	Population Stability Index	Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...	['datasets', 'model']	{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.PrecisionRecallCurve	Precision Recall Curve	Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....	['model', 'dataset']	{}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.ROCCurve	ROC Curve	Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...	['model', 'dataset']	{}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.RegressionErrors	Regression Errors	Assesses the performance and error distribution of a regression model using various error metrics....	['model', 'dataset']	{}	['sklearn', 'model_performance']	['regression', 'classification']
validmind.model_validation.sklearn.RegressionErrorsComparison	Regression Errors Comparison	Assesses multiple regression error metrics to compare model performance across different datasets, emphasizing...	['datasets', 'models']	{}	['model_performance', 'sklearn']	['regression', 'time_series_forecasting']
validmind.model_validation.sklearn.RegressionPerformance	Regression Performance	Evaluates the performance of a regression model using five different metrics: MAE, MSE, RMSE, MAPE, and MBD....	['model', 'dataset']	{}	['sklearn', 'model_performance']	['regression']
validmind.model_validation.sklearn.RegressionR2Square	Regression R2 Square	Assesses the overall goodness-of-fit of a regression model by evaluating R-squared (R2) and Adjusted R-squared (Adj...	['dataset', 'model']	{}	['sklearn', 'model_performance']	['regression']
validmind.model_validation.sklearn.RegressionR2SquareComparison	Regression R2 Square Comparison	Compares R-Squared and Adjusted R-Squared values for different regression models across multiple datasets to assess...	['datasets', 'models']	{}	['model_performance', 'sklearn']	['regression', 'time_series_forecasting']
validmind.model_validation.sklearn.RobustnessDiagnosis	Robustness Diagnosis	Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....	['datasets', 'model']	{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}	['sklearn', 'model_diagnosis', 'visualization']	['classification', 'regression']
validmind.model_validation.sklearn.SHAPGlobalImportance	SHAP Global Importance	Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....	['model', 'dataset']	{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}	['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.ScoreProbabilityAlignment	Score Probability Alignment	Analyzes the alignment between credit scores and predicted probabilities....	['model', 'dataset']	{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}	['visualization', 'credit_risk', 'calibration']	['classification']
validmind.model_validation.sklearn.SilhouettePlot	Silhouette Plot	Calculates and visualizes Silhouette Score, assessing the degree of data point suitability to its cluster in ML...	['model', 'dataset']	{}	['sklearn', 'model_performance']	['clustering']
validmind.model_validation.sklearn.TrainingTestDegradation	Training Test Degradation	Tests if model performance degradation between training and test datasets exceeds a predefined threshold....	['datasets', 'model']	{'max_threshold': {'type': 'float', 'default': 0.1}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.VMeasure	V Measure	Evaluates homogeneity and completeness of a clustering model using the V Measure Score....	['dataset', 'model']	{}	['sklearn', 'model_performance']	['clustering']
validmind.model_validation.sklearn.WeakspotsDiagnosis	Weakspots Diagnosis	Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...	['datasets', 'model']	{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']	['classification', 'text_classification']
validmind.ongoing_monitoring.CalibrationCurveDrift	Calibration Curve Drift	Evaluates changes in probability calibration between reference and monitoring datasets....	['datasets', 'model']	{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.ongoing_monitoring.ClassDiscriminationDrift	Class Discrimination Drift	Compares classification discrimination metrics between reference and monitoring datasets....	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.ongoing_monitoring.ClassificationAccuracyDrift	Classification Accuracy Drift	Compares classification accuracy metrics between reference and monitoring datasets....	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.ongoing_monitoring.ConfusionMatrixDrift	Confusion Matrix Drift	Compares confusion matrix metrics between reference and monitoring datasets....	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.ongoing_monitoring.ROCCurveDrift	ROC Curve Drift	Compares ROC curves between reference and monitoring datasets....	['datasets', 'model']	{}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']

The task parameter is designed for pinpointing tests that align with a specific task type. For instance, to find tests tailored for 'classification' tasks:

list_tests(task="classification")

ID	Name	Description	Required Inputs	Params	Tags	Tasks
validmind.data_validation.BivariateScatterPlots	Bivariate Scatter Plots	Generates bivariate scatterplots to visually inspect relationships between pairs of numerical predictor variables...	['dataset']	{}	['tabular_data', 'numerical_data', 'visualization']	['classification']
validmind.data_validation.ChiSquaredFeaturesTable	Chi Squared Features Table	Assesses the statistical association between categorical features and a target variable using the Chi-Squared test....	['dataset']	{'p_threshold': {'type': '_empty', 'default': 0.05}}	['tabular_data', 'categorical_data', 'statistical_test']	['classification']
validmind.data_validation.ClassImbalance	Class Imbalance	Evaluates and quantifies class distribution imbalance in a dataset used by a machine learning model....	['dataset']	{'min_percent_threshold': {'type': 'int', 'default': 10}}	['tabular_data', 'binary_classification', 'multiclass_classification', 'data_quality']	['classification']
validmind.data_validation.DatasetDescription	Dataset Description	Provides comprehensive analysis and statistical summaries of each column in a machine learning model's dataset....	['dataset']	{}	['tabular_data', 'time_series_data', 'text_data']	['classification', 'regression', 'text_classification', 'text_summarization']
validmind.data_validation.DatasetSplit	Dataset Split	Evaluates and visualizes the distribution proportions among training, testing, and validation datasets of an ML...	['datasets']	{}	['tabular_data', 'time_series_data', 'text_data']	['classification', 'regression', 'text_classification', 'text_summarization']
validmind.data_validation.DescriptiveStatistics	Descriptive Statistics	Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's...	['dataset']	{}	['tabular_data', 'time_series_data', 'data_quality']	['classification', 'regression']
validmind.data_validation.Duplicates	Duplicates	Tests dataset for duplicate entries, ensuring model reliability via data quality verification....	['dataset']	{'min_threshold': {'type': '_empty', 'default': 1}}	['tabular_data', 'data_quality', 'text_data']	['classification', 'regression']
validmind.data_validation.FeatureTargetCorrelationPlot	Feature Target Correlation Plot	Visualizes the correlation between input features and the model's target output in a color-coded horizontal bar...	['dataset']	{'fig_height': {'type': '_empty', 'default': 600}}	['tabular_data', 'visualization', 'correlation']	['classification', 'regression']
validmind.data_validation.HighCardinality	High Cardinality	Assesses the number of unique values in categorical columns to detect high cardinality and potential overfitting....	['dataset']	{'num_threshold': {'type': 'int', 'default': 100}, 'percent_threshold': {'type': 'float', 'default': 0.1}, 'threshold_type': {'type': 'str', 'default': 'percent'}}	['tabular_data', 'data_quality', 'categorical_data']	['classification', 'regression']
validmind.data_validation.HighPearsonCorrelation	High Pearson Correlation	Identifies highly correlated feature pairs in a dataset suggesting feature redundancy or multicollinearity....	['dataset']	{'max_threshold': {'type': 'float', 'default': 0.3}, 'top_n_correlations': {'type': 'int', 'default': 10}, 'feature_columns': {'type': 'list', 'default': None}}	['tabular_data', 'data_quality', 'correlation']	['classification', 'regression']
validmind.data_validation.IQROutliersBarPlot	IQR Outliers Bar Plot	Visualizes outlier distribution across percentiles in numerical data using the Interquartile Range (IQR) method....	['dataset']	{'threshold': {'type': 'float', 'default': 1.5}, 'fig_width': {'type': 'int', 'default': 800}}	['tabular_data', 'visualization', 'numerical_data']	['classification', 'regression']
validmind.data_validation.IQROutliersTable	IQR Outliers Table	Determines and summarizes outliers in numerical features using the Interquartile Range method....	['dataset']	{'threshold': {'type': 'float', 'default': 1.5}}	['tabular_data', 'numerical_data']	['classification', 'regression']
validmind.data_validation.IsolationForestOutliers	Isolation Forest Outliers	Detects outliers in a dataset using the Isolation Forest algorithm and visualizes results through scatter plots....	['dataset']	{'random_state': {'type': 'int', 'default': 0}, 'contamination': {'type': 'float', 'default': 0.1}, 'feature_columns': {'type': 'list', 'default': None}}	['tabular_data', 'anomaly_detection']	['classification']
validmind.data_validation.JarqueBera	Jarque Bera	Assesses normality of dataset features in an ML model using the Jarque-Bera test....	['dataset']	{}	['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']	['classification', 'regression']
validmind.data_validation.MissingValues	Missing Values	Evaluates dataset quality by ensuring missing value ratio across all features does not exceed a set threshold....	['dataset']	{'min_threshold': {'type': 'int', 'default': 1}}	['tabular_data', 'data_quality']	['classification', 'regression']
validmind.data_validation.MissingValuesBarPlot	Missing Values Bar Plot	Assesses the percentage and distribution of missing values in the dataset via a bar plot, with emphasis on...	['dataset']	{'threshold': {'type': 'int', 'default': 80}, 'fig_height': {'type': 'int', 'default': 600}}	['tabular_data', 'data_quality', 'visualization']	['classification', 'regression']
validmind.data_validation.MutualInformation	Mutual Information	Calculates mutual information scores between features and target variable to evaluate feature relevance....	['dataset']	{'min_threshold': {'type': 'float', 'default': 0.01}, 'task': {'type': 'str', 'default': 'classification'}}	['feature_selection', 'data_analysis']	['classification', 'regression']
validmind.data_validation.PearsonCorrelationMatrix	Pearson Correlation Matrix	Evaluates linear dependency between numerical variables in a dataset via a Pearson Correlation coefficient heat map....	['dataset']	{}	['tabular_data', 'numerical_data', 'correlation']	['classification', 'regression']
validmind.data_validation.ProtectedClassesDescription	Protected Classes Description	Visualizes the distribution of protected classes in the dataset relative to the target variable...	['dataset']	{'protected_classes': {'type': '_empty', 'default': None}}	['bias_and_fairness', 'descriptive_statistics']	['classification', 'regression']
validmind.data_validation.RunsTest	Runs Test	Executes Runs Test on ML model to detect non-random patterns in output data sequence....	['dataset']	{}	['tabular_data', 'statistical_test', 'statsmodels']	['classification', 'regression']
validmind.data_validation.ScatterPlot	Scatter Plot	Assesses visual relationships, patterns, and outliers among features in a dataset through scatter plot matrices....	['dataset']	{}	['tabular_data', 'visualization']	['classification', 'regression']
validmind.data_validation.ScoreBandDefaultRates	Score Band Default Rates	Analyzes default rates and population distribution across credit score bands....	['dataset', 'model']	{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}}	['visualization', 'credit_risk', 'scorecard']	['classification']
validmind.data_validation.ShapiroWilk	Shapiro Wilk	Evaluates feature-wise normality of training data using the Shapiro-Wilk test....	['dataset']	{}	['tabular_data', 'data_distribution', 'statistical_test']	['classification', 'regression']
validmind.data_validation.Skewness	Skewness	Evaluates the skewness of numerical data in a dataset to check against a defined threshold, aiming to ensure data...	['dataset']	{'max_threshold': {'type': '_empty', 'default': 1}}	['data_quality', 'tabular_data']	['classification', 'regression']
validmind.data_validation.TabularCategoricalBarPlots	Tabular Categorical Bar Plots	Generates and visualizes bar plots for each category in categorical features to evaluate the dataset's composition....	['dataset']	{}	['tabular_data', 'visualization']	['classification', 'regression']
validmind.data_validation.TabularDateTimeHistograms	Tabular Date Time Histograms	Generates histograms to provide graphical insight into the distribution of time intervals in a model's datetime...	['dataset']	{}	['time_series_data', 'visualization']	['classification', 'regression']
validmind.data_validation.TabularDescriptionTables	Tabular Description Tables	Summarizes key descriptive statistics for numerical, categorical, and datetime variables in a dataset....	['dataset']	{}	['tabular_data']	['classification', 'regression']
validmind.data_validation.TabularNumericalHistograms	Tabular Numerical Histograms	Generates histograms for each numerical feature in a dataset to provide visual insights into data distribution and...	['dataset']	{}	['tabular_data', 'visualization']	['classification', 'regression']
validmind.data_validation.TargetRateBarPlots	Target Rate Bar Plots	Generates bar plots visualizing the default rates of categorical features for a classification machine learning...	['dataset']	{}	['tabular_data', 'visualization', 'categorical_data']	['classification']
validmind.data_validation.TooManyZeroValues	Too Many Zero Values	Identifies numerical columns in a dataset that contain an excessive number of zero values, defined by a threshold...	['dataset']	{'max_percent_threshold': {'type': 'float', 'default': 0.03}}	['tabular_data']	['regression', 'classification']
validmind.data_validation.UniqueRows	Unique Rows	Verifies the diversity of the dataset by ensuring that the count of unique rows exceeds a prescribed threshold....	['dataset']	{'min_percent_threshold': {'type': 'float', 'default': 1}}	['tabular_data']	['regression', 'classification']
validmind.data_validation.WOEBinPlots	WOE Bin Plots	Generates visualizations of Weight of Evidence (WoE) and Information Value (IV) for understanding predictive power...	['dataset']	{'breaks_adj': {'type': 'list', 'default': None}, 'fig_height': {'type': 'int', 'default': 600}, 'fig_width': {'type': 'int', 'default': 500}}	['tabular_data', 'visualization', 'categorical_data']	['classification']
validmind.data_validation.WOEBinTable	WOE Bin Table	Assesses the Weight of Evidence (WoE) and Information Value (IV) of each feature to evaluate its predictive power...	['dataset']	{'breaks_adj': {'type': 'list', 'default': None}}	['tabular_data', 'categorical_data']	['classification']
validmind.model_validation.FeaturesAUC	Features AUC	Evaluates the discriminatory power of each individual feature within a binary classification model by calculating...	['dataset']	{'fontsize': {'type': 'int', 'default': 12}, 'figure_height': {'type': 'int', 'default': 500}}	['feature_importance', 'AUC', 'visualization']	['classification']
validmind.model_validation.sklearn.CalibrationCurve	Calibration Curve	Evaluates the calibration of probability estimates by comparing predicted probabilities against observed...	['model', 'dataset']	{'n_bins': {'type': 'int', 'default': 10}}	['sklearn', 'model_performance', 'classification']	['classification']
validmind.model_validation.sklearn.ClassifierPerformance	Classifier Performance	Evaluates performance of binary or multiclass classification models using precision, recall, F1-Score, accuracy,...	['dataset', 'model']	{'average': {'type': 'str', 'default': 'macro'}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.ClassifierThresholdOptimization	Classifier Threshold Optimization	Analyzes and visualizes different threshold optimization methods for binary classification models....	['dataset', 'model']	{'methods': {'type': None, 'default': None}, 'target_recall': {'type': None, 'default': None}}	['model_validation', 'threshold_optimization', 'classification_metrics']	['classification']
validmind.model_validation.sklearn.ConfusionMatrix	Confusion Matrix	Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...	['dataset', 'model']	{'threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.HyperParametersTuning	Hyper Parameters Tuning	Performs exhaustive grid search over specified parameter ranges to find optimal model configurations...	['model', 'dataset']	{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}, 'fit_params': {'type': 'dict', 'default': None}}	['sklearn', 'model_performance']	['clustering', 'classification']
validmind.model_validation.sklearn.MinimumAccuracy	Minimum Accuracy	Checks if the model's prediction accuracy meets or surpasses a specified threshold....	['dataset', 'model']	{'min_threshold': {'type': 'float', 'default': 0.7}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.MinimumF1Score	Minimum F1 Score	Assesses if the model's F1 score on the validation set meets a predefined minimum threshold, ensuring balanced...	['dataset', 'model']	{'min_threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.MinimumROCAUCScore	Minimum ROCAUC Score	Validates model by checking if the ROC AUC score meets or surpasses a specified threshold....	['dataset', 'model']	{'min_threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.ModelParameters	Model Parameters	Extracts and displays model parameters in a structured format for transparency and reproducibility....	['model']	{'model_params': {'type': '_empty', 'default': None}}	['model_training', 'metadata']	['classification', 'regression']
validmind.model_validation.sklearn.ModelsPerformanceComparison	Models Performance Comparison	Evaluates and compares the performance of multiple Machine Learning models using various metrics like accuracy,...	['dataset', 'models']	{}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'model_comparison']	['classification', 'text_classification']
validmind.model_validation.sklearn.OverfitDiagnosis	Overfit Diagnosis	Assesses potential overfitting in a model's predictions, identifying regions where performance between training and...	['model', 'datasets']	{'metric': {'type': 'str', 'default': None}, 'cut_off_threshold': {'type': 'float', 'default': 0.04}}	['sklearn', 'binary_classification', 'multiclass_classification', 'linear_regression', 'model_diagnosis']	['classification', 'regression']
validmind.model_validation.sklearn.PermutationFeatureImportance	Permutation Feature Importance	Assesses the significance of each feature in a model by evaluating the impact on model performance when feature...	['model', 'dataset']	{'fontsize': {'type': None, 'default': None}, 'figure_height': {'type': None, 'default': None}}	['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.PopulationStabilityIndex	Population Stability Index	Assesses the Population Stability Index (PSI) to quantify the stability of an ML model's predictions across...	['datasets', 'model']	{'num_bins': {'type': 'int', 'default': 10}, 'mode': {'type': 'str', 'default': 'fixed'}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.model_validation.sklearn.PrecisionRecallCurve	Precision Recall Curve	Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....	['model', 'dataset']	{}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.ROCCurve	ROC Curve	Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...	['model', 'dataset']	{}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.RegressionErrors	Regression Errors	Assesses the performance and error distribution of a regression model using various error metrics....	['model', 'dataset']	{}	['sklearn', 'model_performance']	['regression', 'classification']
validmind.model_validation.sklearn.RobustnessDiagnosis	Robustness Diagnosis	Assesses the robustness of a machine learning model by evaluating performance decay under noisy conditions....	['datasets', 'model']	{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': None, 'default': [0.1, 0.2, 0.3, 0.4, 0.5]}, 'performance_decay_threshold': {'type': 'float', 'default': 0.05}}	['sklearn', 'model_diagnosis', 'visualization']	['classification', 'regression']
validmind.model_validation.sklearn.SHAPGlobalImportance	SHAP Global Importance	Evaluates and visualizes global feature importance using SHAP values for model explanation and risk identification....	['model', 'dataset']	{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': None, 'default': None}}	['sklearn', 'binary_classification', 'multiclass_classification', 'feature_importance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.ScoreProbabilityAlignment	Score Probability Alignment	Analyzes the alignment between credit scores and predicted probabilities....	['model', 'dataset']	{'score_column': {'type': 'str', 'default': 'score'}, 'n_bins': {'type': 'int', 'default': 10}}	['visualization', 'credit_risk', 'calibration']	['classification']
validmind.model_validation.sklearn.TrainingTestDegradation	Training Test Degradation	Tests if model performance degradation between training and test datasets exceeds a predefined threshold....	['datasets', 'model']	{'max_threshold': {'type': 'float', 'default': 0.1}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.WeakspotsDiagnosis	Weakspots Diagnosis	Identifies and visualizes weak spots in a machine learning model's performance across various sections of the...	['datasets', 'model']	{'features_columns': {'type': None, 'default': None}, 'metrics': {'type': None, 'default': None}, 'thresholds': {'type': None, 'default': None}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_diagnosis', 'visualization']	['classification', 'text_classification']
validmind.model_validation.statsmodels.CumulativePredictionProbabilities	Cumulative Prediction Probabilities	Visualizes cumulative probabilities of positive and negative classes for both training and testing in classification models....	['dataset', 'model']	{'title': {'type': '_empty', 'default': 'Cumulative Probabilities'}}	['visualization', 'credit_risk']	['classification']
validmind.model_validation.statsmodels.GINITable	GINI Table	Evaluates classification model performance using AUC, GINI, and KS metrics for training and test datasets....	['dataset', 'model']	{}	['model_performance']	['classification']
validmind.model_validation.statsmodels.KolmogorovSmirnov	Kolmogorov Smirnov	Assesses whether each feature in the dataset aligns with a normal distribution using the Kolmogorov-Smirnov test....	['model', 'dataset']	{'dist': {'type': 'str', 'default': 'norm'}}	['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']	['classification', 'regression']
validmind.model_validation.statsmodels.Lilliefors	Lilliefors	Assesses the normality of feature distributions in an ML model's training dataset using the Lilliefors test....	['dataset']	{}	['tabular_data', 'data_distribution', 'statistical_test', 'statsmodels']	['classification', 'regression']
validmind.model_validation.statsmodels.PredictionProbabilitiesHistogram	Prediction Probabilities Histogram	Assesses the predictive probability distribution for binary classification to evaluate model performance and...	['dataset', 'model']	{'title': {'type': '_empty', 'default': 'Histogram of Predictive Probabilities'}}	['visualization', 'credit_risk']	['classification']
validmind.model_validation.statsmodels.ScorecardHistogram	Scorecard Histogram	The Scorecard Histogram test evaluates the distribution of credit scores between default and non-default instances,...	['dataset']	{'title': {'type': '_empty', 'default': 'Histogram of Scores'}, 'score_column': {'type': '_empty', 'default': 'score'}}	['visualization', 'credit_risk', 'logistic_regression']	['classification']
validmind.ongoing_monitoring.CalibrationCurveDrift	Calibration Curve Drift	Evaluates changes in probability calibration between reference and monitoring datasets....	['datasets', 'model']	{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.ongoing_monitoring.ClassDiscriminationDrift	Class Discrimination Drift	Compares classification discrimination metrics between reference and monitoring datasets....	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.ongoing_monitoring.ClassImbalanceDrift	Class Imbalance Drift	Evaluates drift in class distribution between reference and monitoring datasets....	['datasets']	{'drift_pct_threshold': {'type': 'float', 'default': 5.0}, 'title': {'type': 'str', 'default': 'Class Distribution Drift'}}	['tabular_data', 'binary_classification', 'multiclass_classification']	['classification']
validmind.ongoing_monitoring.ClassificationAccuracyDrift	Classification Accuracy Drift	Compares classification accuracy metrics between reference and monitoring datasets....	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.ongoing_monitoring.ConfusionMatrixDrift	Confusion Matrix Drift	Compares confusion matrix metrics between reference and monitoring datasets....	['datasets', 'model']	{'drift_pct_threshold': {'type': '_empty', 'default': 20}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance']	['classification', 'text_classification']
validmind.ongoing_monitoring.CumulativePredictionProbabilitiesDrift	Cumulative Prediction Probabilities Drift	Compares cumulative prediction probability distributions between reference and monitoring datasets....	['datasets', 'model']	{}	['visualization', 'credit_risk']	['classification']
validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift	Prediction Probabilities Histogram Drift	Compares prediction probability distributions between reference and monitoring datasets....	['datasets', 'model']	{'title': {'type': '_empty', 'default': 'Prediction Probabilities Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}	['visualization', 'credit_risk']	['classification']
validmind.ongoing_monitoring.ROCCurveDrift	ROC Curve Drift	Compares ROC curves between reference and monitoring datasets....	['datasets', 'model']	{}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.ongoing_monitoring.ScoreBandsDrift	Score Bands Drift	Analyzes drift in population distribution and default rates across score bands....	['datasets', 'model']	{'score_column': {'type': 'str', 'default': 'score'}, 'score_bands': {'type': 'list', 'default': None}, 'drift_threshold': {'type': 'float', 'default': 20.0}}	['visualization', 'credit_risk', 'scorecard']	['classification']
validmind.ongoing_monitoring.ScorecardHistogramDrift	Scorecard Histogram Drift	Compares score distributions between reference and monitoring datasets for each class....	['datasets']	{'score_column': {'type': 'str', 'default': 'score'}, 'title': {'type': 'str', 'default': 'Scorecard Histogram Drift'}, 'drift_pct_threshold': {'type': 'float', 'default': 20.0}}	['visualization', 'credit_risk', 'logistic_regression']	['classification']
validmind.unit_metrics.classification.Accuracy	Accuracy	Calculates the accuracy of a model	['dataset', 'model']	{}	['classification']	['classification']
validmind.unit_metrics.classification.F1	F1	Calculates the F1 score for a classification model.	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.Precision	Precision	Calculates the precision for a classification model.	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.ROC_AUC	ROC AUC	Calculates the ROC AUC for a classification model.	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.Recall	Recall	Calculates the recall for a classification model.	['model', 'dataset']	{}	['classification']	['classification']

The tags parameter facilitates searching tests by their tags. For instance, if you're interested in only tests associated designed for model_performance that produce a plot (denoted by the visualization tag)

list_tests(tags=["model_performance", "visualization"])

ID	Name	Description	Required Inputs	Params	Tags	Tasks
validmind.model_validation.RegressionResidualsPlot	Regression Residuals Plot	Evaluates regression model performance using residual distribution and actual vs. predicted plots....	['model', 'dataset']	{'bin_size': {'type': 'float', 'default': 0.1}}	['model_performance', 'visualization']	['regression']
validmind.model_validation.sklearn.ConfusionMatrix	Confusion Matrix	Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...	['dataset', 'model']	{'threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.PrecisionRecallCurve	Precision Recall Curve	Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....	['model', 'dataset']	{}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.ROCCurve	ROC Curve	Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...	['model', 'dataset']	{}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.TrainingTestDegradation	Training Test Degradation	Tests if model performance degradation between training and test datasets exceeds a predefined threshold....	['datasets', 'model']	{'max_threshold': {'type': 'float', 'default': 0.1}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.ongoing_monitoring.CalibrationCurveDrift	Calibration Curve Drift	Evaluates changes in probability calibration between reference and monitoring datasets....	['datasets', 'model']	{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.ongoing_monitoring.ROCCurveDrift	ROC Curve Drift	Compares ROC curves between reference and monitoring datasets....	['datasets', 'model']	{}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']

The above parameters can be combined to create complex queries. For instance, to find tests that are compatible with 'sklearn' models, designed for 'classification' tasks, and produce a plot:

list_tests(
    tags=["model_performance", "visualization", "sklearn"], task="classification"
)

ID	Name	Description	Required Inputs	Params	Tags	Tasks
validmind.model_validation.sklearn.ConfusionMatrix	Confusion Matrix	Evaluates and visually represents the classification ML model's predictive performance using a Confusion Matrix...	['dataset', 'model']	{'threshold': {'type': 'float', 'default': 0.5}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.PrecisionRecallCurve	Precision Recall Curve	Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve....	['model', 'dataset']	{}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.ROCCurve	ROC Curve	Evaluates binary classification model performance by generating and plotting the Receiver Operating Characteristic...	['model', 'dataset']	{}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.model_validation.sklearn.TrainingTestDegradation	Training Test Degradation	Tests if model performance degradation between training and test datasets exceeds a predefined threshold....	['datasets', 'model']	{'max_threshold': {'type': 'float', 'default': 0.1}}	['sklearn', 'binary_classification', 'multiclass_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.ongoing_monitoring.CalibrationCurveDrift	Calibration Curve Drift	Evaluates changes in probability calibration between reference and monitoring datasets....	['datasets', 'model']	{'n_bins': {'type': 'int', 'default': 10}, 'drift_pct_threshold': {'type': 'float', 'default': 20}}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']
validmind.ongoing_monitoring.ROCCurveDrift	ROC Curve Drift	Compares ROC curves between reference and monitoring datasets....	['datasets', 'model']	{}	['sklearn', 'binary_classification', 'model_performance', 'visualization']	['classification', 'text_classification']

Programmatic Use

To work with a specific set of tests programmatically, you can store the results in a variable. For instance, let's list all tests that are designed for Text Summarization tests and store them in text_summarization_tests for further use.

text_summarization_tests = list_tests(task="text_summarization", pretty=False)
text_summarization_tests

['validmind.data_validation.DatasetDescription',
 'validmind.data_validation.DatasetSplit',
 'validmind.data_validation.nlp.CommonWords',
 'validmind.data_validation.nlp.Hashtags',
 'validmind.data_validation.nlp.LanguageDetection',
 'validmind.data_validation.nlp.Mentions',
 'validmind.data_validation.nlp.Punctuations',
 'validmind.data_validation.nlp.StopWords',
 'validmind.data_validation.nlp.TextDescription',
 'validmind.model_validation.BertScore',
 'validmind.model_validation.BleuScore',
 'validmind.model_validation.ContextualRecall',
 'validmind.model_validation.MeteorScore',
 'validmind.model_validation.RegardScore',
 'validmind.model_validation.RougeScore',
 'validmind.model_validation.TokenDisparity',
 'validmind.model_validation.ToxicityScore',
 'validmind.model_validation.embeddings.CosineSimilarityComparison',
 'validmind.model_validation.embeddings.CosineSimilarityHeatmap',
 'validmind.model_validation.embeddings.EuclideanDistanceComparison',
 'validmind.model_validation.embeddings.EuclideanDistanceHeatmap',
 'validmind.model_validation.embeddings.PCAComponentsPairwisePlots',
 'validmind.model_validation.embeddings.TSNEComponentsPairwisePlots',
 'validmind.model_validation.ragas.AnswerCorrectness',
 'validmind.model_validation.ragas.AspectCritic',
 'validmind.model_validation.ragas.ContextEntityRecall',
 'validmind.model_validation.ragas.ContextPrecision',
 'validmind.model_validation.ragas.ContextPrecisionWithoutReference',
 'validmind.model_validation.ragas.ContextRecall',
 'validmind.model_validation.ragas.Faithfulness',
 'validmind.model_validation.ragas.NoiseSensitivity',
 'validmind.model_validation.ragas.ResponseRelevancy',
 'validmind.model_validation.ragas.SemanticSimilarity',
 'validmind.prompt_validation.Bias',
 'validmind.prompt_validation.Clarity',
 'validmind.prompt_validation.Conciseness',
 'validmind.prompt_validation.Delimitation',
 'validmind.prompt_validation.NegativeInstruction',
 'validmind.prompt_validation.Robustness',
 'validmind.prompt_validation.Specificity']

Delving into Test Details with `describe_test`

After identifying a set of potential tests, you might want to explore the specifics of an individual test. The describe_test function provides a deep dive into the details of a test. It reveals the test name, description, ID, test type, and required inputs. Below, we showcase how to describe a test using its ID:

describe_test("validmind.model_validation.sklearn.OverfitDiagnosis")

Next steps

By harnessing the functionalities presented in this guide, you should be able to easily list and filter through all of ValidMind's available tests and find those you are interested in running against your model and/or dataset. The next step is to take the IDs of the tests you'd like to run and either create a test suite for reuse or just run them directly to try them out. See the other notebooks for a tutorial on how to do both.

Discover more learning resources

We offer many interactive notebooks to help you document models:

Or, visit our documentation to learn more about ValidMind.

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