ValidMind for model validation 1 — Set up the ValidMind Library for validation

Learn how to use ValidMind for your end-to-end model validation process based on common scenarios with our series of four introductory notebooks. In this first notebook, set up the ValidMind Library in preparation for validating a champion model.

These notebooks use a binary classification model as an example, but the same principles shown here apply to other model types.

Learn by doing

Our course tailor-made for validators new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Validator Fundamentals

Introduction

Model validation aims to independently assess the compliance of champion models created by model developers with regulatory guidance by conducting thorough testing and analysis, potentially including the use of challenger models to benchmark performance. Assessments, presented in the form of a validation report, typically include model findings and recommendations to address those issues.

A binary classification model is a type of predictive model used in churn analysis to identify customers who are likely to leave a service or subscription by analyzing various behavioral, transactional, and demographic factors.

• This model helps businesses take proactive measures to retain at-risk customers by offering personalized incentives, improving customer service, or adjusting pricing strategies.
• Effective validation of a churn prediction model ensures that businesses can accurately identify potential churners, optimize retention efforts, and enhance overall customer satisfaction while minimizing revenue loss.

About ValidMind

ValidMind is a suite of tools for managing model risk, including risk associated with AI and statistical models.

You use the ValidMind Library to automate comparison and other validation tests, and then use the ValidMind Platform to submit compliance assessments of champion models via comprehensive validation reports. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model developers.

Before you begin

This notebook assumes you have basic familiarity with Python, including an understanding of how functions work. If you are new to Python, you can still run the notebook but we recommend further familiarizing yourself with the language.

If you encounter errors due to missing modules in your Python environment, install the modules with pip install, and then re-run the notebook. For more help, refer to Installing Python Modules.

New to ValidMind?

If you haven't already seen our documentation on the ValidMind Library, we recommend you begin by exploring the available resources in this section. There, you can learn more about documenting models and running tests, as well as find code samples and our Python Library API reference.

For access to all features available in this notebook, you'll need access to a ValidMind account.

Register with ValidMind

Key concepts

Validation report: A comprehensive and structured assessment of a model’s development and performance, focusing on verifying its integrity, appropriateness, and alignment with its intended use. It includes analyses of model assumptions, data quality, performance metrics, outcomes of testing procedures, and risk considerations. The validation report supports transparency, regulatory compliance, and informed decision-making by documenting the validator’s independent review and conclusions.

Validation report template: Serves as a standardized framework for conducting and documenting model validation activities. It outlines the required sections, recommended analyses, and expected validation tests, ensuring consistency and completeness across validation reports. The template helps guide validators through a systematic review process while promoting comparability and traceability of validation outcomes.

Tests: A function contained in the ValidMind Library, designed to run a specific quantitative test on the dataset or model. Tests are the building blocks of ValidMind, used to evaluate and document models and datasets.

Metrics: A subset of tests that do not have thresholds. In the context of this notebook, metrics and tests can be thought of as interchangeable concepts.

Custom metrics: Custom metrics are functions that you define to evaluate your model or dataset. These functions can be registered with the ValidMind Library to be used in the ValidMind Platform.

Inputs: Objects to be evaluated and documented in the ValidMind Library. They can be any of the following:

• model: A single model that has been initialized in ValidMind with vm.init_model().
• dataset: Single dataset that has been initialized in ValidMind with vm.init_dataset().
• models: A list of ValidMind models - usually this is used when you want to compare multiple models in your custom metric.
• datasets: A list of ValidMind datasets - usually this is used when you want to compare multiple datasets in your custom metric. (Learn more: Run tests with multiple datasets)

Parameters: Additional arguments that can be passed when running a ValidMind test, used to pass additional information to a metric, customize its behavior, or provide additional context.

Outputs: Custom metrics can return elements like tables or plots. Tables may be a list of dictionaries (each representing a row) or a pandas DataFrame. Plots may be matplotlib or plotly figures.

Setting up

Register a sample model

In a usual model lifecycle, a champion model will have been independently registered in your model inventory and submitted to you for validation by your model development team as part of the effective challenge process. (Learn more: Submit for approval)

For this series of notebooks, we'll have you register a dummy model in the ValidMind Platform inventory and assign yourself as the validator to familiarize you with the ValidMind interface and circumvent the need for an existing model:

1. In a browser, log in to ValidMind.

2. In the left sidebar, navigate to Inventory and click + Register Model.

3. Enter the model details and click Continue. (Need more help?)

   For example, to register a model for use with this notebook, select:

   • Documentation template: Binary classification
   • Use case: Marketing/Sales - Attrition/Churn Management

   You can fill in other options according to your preference.

Assign validator credentials

In order to log tests as a validator instead of as a developer, on the model details page that appears after you've successfully registered your sample model:

1. Remove yourself as a model owner:

   • Click on the OWNERS tile.
   • Click the x next to your name to remove yourself from that model's role.
   • Click Save to apply your changes to that role.

2. Remove yourself as a developer:

   • Click on the DEVELOPERS tile.
   • Click the x next to your name to remove yourself from that model's role.
   • Click Save to apply your changes to that role.

3. Add yourself as a validator:

   • Click on the VALIDATORS tile.
   • Select your name from the drop-down menu.
   • Click Save to apply your changes to that role.

Install the ValidMind Library

Recommended Python versions

Python 3.8 <= x <= 3.11

To install the library:

%pip install -q validmind

Note: you may need to restart the kernel to use updated packages.

Initialize the ValidMind Library

ValidMind generates a unique code snippet for each registered model to connect with your validation environment. You initialize the ValidMind Library with this code snippet, which ensures that your test results are uploaded to the correct model when you run the notebook.

Get your code snippet

1. In a browser, log in to ValidMind.

2. In the left sidebar, navigate to Inventory and select the model you registered for this "ValidMind for model validation" series of notebooks.

3. Go to Getting Started and click Copy snippet to clipboard.

Next, load your model identifier credentials from an .env file or replace the placeholder with your own code snippet:

# Load your model identifier credentials from an `.env` file

%load_ext dotenv
%dotenv .env

# Or replace with your code snippet

import validmind as vm

vm.init(
    # api_host="...",
    # api_key="...",
    # api_secret="...",
    # model="...",
)

⚠️ This kernel is running an older version of validmind (2.9.2) than the latest version installed on your system (2.9.3). You may need to restart the kernel if you are experiencing issues.

2025-09-10 15:56:36,722 - INFO(validmind.api_client): 🎉 Connected to ValidMind!
📊 Model: [ValidMind Academy] Model validation (ID: cmalguc9y02ok199q2db381ib)
📁 Document Type: validation_report

Getting to know ValidMind

Preview the validation report template

Let's verify that you have connected the ValidMind Library to the ValidMind Platform and that the appropriate template is selected for model validation. A template predefines sections for your validation report and provides a general outline to follow, making the validation process much easier.

You will attach evidence to this template in the form of risk assessment notes, findings, and test results later on. For now, take a look at the default structure that the template provides with the vm.preview_template() function from the ValidMind library:

vm.preview_template()

View validation report in the ValidMind Platform

Next, let's head to the ValidMind Platform to see the template in action:

1. In a browser, log in to ValidMind.

2. In the left sidebar, navigate to Inventory and select the model you registered for this "ValidMind for model validation" series of notebooks.

3. Click Validation Report under Documents for your model and note:

   • The risk assessment compliance summary at the top of the report (screenshot below)
   • How the structure of the validation report reflects the previewed template

   
   
   

Explore available tests

Next, let's explore the list of all available tests in the ValidMind Library with the vm.tests.list_tests() function — we'll later narrow down the tests we want to run from this list when we learn to run tests.

vm.tests.list_tests()

ID	Name	Description	Has Figure	Has Table	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...	True	False	['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....	False	True	['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....	False	True	['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...	False	True	['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....	False	True	['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...	True	False	['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....	False	True	['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....	False	True	['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....	True	True	['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....	False	True	['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...	False	True	['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...	False	True	['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....	False	True	['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....	False	True	['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....	False	True	['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...	True	False	['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....	False	True	['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....	False	True	['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....	True	False	['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....	False	True	['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....	True	False	['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....	False	True	['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....	False	True	['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....	False	True	['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...	True	False	['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....	False	True	['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...	True	False	['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....	True	False	['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....	True	False	['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....	False	True	['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...	True	True	['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...	True	False	['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....	False	True	['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....	True	False	['dataset']	{}	['tabular_data', 'visualization']	['classification', 'regression']
validmind.data_validation.ScoreBandDefaultRates	Score Band Default Rates	Analyzes default rates and population distribution across credit score bands....	False	True	['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....	True	False	['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....	False	True	['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...	False	True	['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...	True	False	['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....	True	False	['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...	True	False	['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....	False	True	['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...	True	False	['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...	True	False	['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,...	False	True	['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....	False	True	['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....	True	True	['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....	True	False	['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....	True	False	['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....	True	True	['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....	False	True	['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...	False	True	['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....	False	True	['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...	True	False	['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...	False	True	['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....	False	True	['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....	True	False	['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....	True	False	['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....	True	False	['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....	True	False	['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....	True	True	['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....	True	False	['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....	True	False	['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....	True	True	['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...	True	False	['dataset']	{'unwanted_tokens': {'type': 'set', 'default': {'mr', "s'", 'ms', 'dr', "''", 's', 'mrs', ' ', '``', 'dollar', "'s", 'us'}}, '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....	True	False	['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...	True	True	['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...	True	True	['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...	True	False	['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...	True	True	['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...	True	False	['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...	True	True	['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....	False	True	['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....	True	True	['dataset', 'model']	{'nbins': {'type': 'int', 'default': 100}, 'p_value_threshold': {'type': 'float', 'default': 0.05}, 'start_date': {'type': 'Optional', 'default': None}, 'end_date': {'type': 'Optional', '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...	True	True	['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....	True	False	['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...	True	True	['dataset', 'model']	{'metric': {'type': 'str', '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...	True	True	['dataset', 'model']	{'confidence': {'type': 'float', '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....	True	False	['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...	True	True	['dataset', 'model']	{'segments': {'type': 'Optional', '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...	True	True	['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....	True	True	['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....	True	False	['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...	True	True	['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...	True	False	['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....	True	False	['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...	True	False	['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....	True	False	['dataset', 'model']	{'cluster_column': {'type': 'Optional', '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...	True	True	['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....	True	False	['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...	True	False	['dataset', 'model']	{'n_components': {'type': 'int', '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....	True	True	['dataset', 'model']	{'keyword_dict': {'type': 'Dict', '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....	True	True	['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....	True	True	['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...	True	True	['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...	True	False	['dataset', 'model']	{'n_components': {'type': 'int', 'default': 2}, 'perplexity': {'type': 'int', 'default': 30}, 'title': {'type': 'str', '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...	True	True	['dataset']	{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'response_column': {'type': 'str', 'default': 'response'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}	['ragas', 'llm']	['text_qa', 'text_generation', 'text_summarization']
validmind.model_validation.ragas.AspectCritic	Aspect Critic	Evaluates generations against the following aspects: harmfulness, maliciousness,...	True	True	['dataset']	{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'response_column': {'type': 'str', 'default': 'response'}, 'retrieved_contexts_column': {'type': 'Optional', 'default': None}, 'aspects': {'type': 'List', 'default': ['coherence', 'conciseness', 'correctness', 'harmfulness', 'maliciousness']}, 'additional_aspects': {'type': 'Optional', 'default': None}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', '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....	True	True	['dataset']	{'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}	['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...	True	True	['dataset']	{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}	['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...	True	True	['dataset']	{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'response_column': {'type': 'str', 'default': 'response'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}	['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...	True	True	['dataset']	{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}	['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....	True	True	['dataset']	{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'response_column': {'type': 'str', 'default': 'response'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}	['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...	True	True	['dataset']	{'response_column': {'type': 'str', 'default': 'response'}, 'retrieved_contexts_column': {'type': 'str', 'default': 'retrieved_contexts'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'focus': {'type': 'str', 'default': 'relevant'}, 'user_input_column': {'type': 'str', 'default': 'user_input'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}	['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....	True	True	['dataset']	{'user_input_column': {'type': 'str', 'default': 'user_input'}, 'retrieved_contexts_column': {'type': 'str', 'default': None}, 'response_column': {'type': 'str', 'default': 'response'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}	['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...	True	True	['dataset']	{'response_column': {'type': 'str', 'default': 'response'}, 'reference_column': {'type': 'str', 'default': 'reference'}, 'judge_llm': {'type': '_empty', 'default': None}, 'judge_embeddings': {'type': '_empty', 'default': None}}	['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...	False	True	['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...	False	True	['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...	True	False	['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,...	False	True	['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....	False	True	['dataset', 'model']	{'methods': {'type': 'Optional', 'default': None}, 'target_recall': {'type': 'Optional', '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....	False	True	['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....	False	True	['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....	False	True	['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...	True	False	['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...	False	True	['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...	False	True	['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...	False	True	['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...	False	True	['model', 'dataset']	{'param_grid': {'type': 'dict', 'default': None}, 'scoring': {'type': 'Union', 'default': None}, 'thresholds': {'type': 'Union', '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....	True	False	['model', 'dataset']	{'n_clusters': {'type': 'Optional', '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....	False	True	['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...	False	True	['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....	False	True	['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....	False	True	['model']	{'model_params': {'type': 'Optional', '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,...	False	True	['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...	True	True	['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...	True	False	['model', 'dataset']	{'fontsize': {'type': 'Optional', 'default': None}, 'figure_height': {'type': 'Optional', '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...	True	True	['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....	True	False	['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...	True	False	['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....	False	True	['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...	False	True	['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....	False	True	['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...	False	True	['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...	False	True	['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....	True	True	['datasets', 'model']	{'metric': {'type': 'str', 'default': None}, 'scaling_factor_std_dev_list': {'type': 'List', '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....	False	True	['model', 'dataset']	{'kernel_explainer_samples': {'type': 'int', 'default': 10}, 'tree_or_linear_explainer_samples': {'type': 'int', 'default': 200}, 'class_of_interest': {'type': 'Optional', '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....	True	True	['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...	True	True	['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....	False	True	['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....	False	True	['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...	True	True	['datasets', 'model']	{'features_columns': {'type': 'Optional', 'default': None}, 'metrics': {'type': 'Optional', 'default': None}, 'thresholds': {'type': 'Optional', '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....	False	True	['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....	True	False	['dataset', 'model']	{'title': {'type': 'str', '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....	False	True	['dataset', 'model']	{'threshold': {'type': 'List', '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....	False	True	['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....	False	True	['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....	False	True	['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...	True	False	['dataset', 'model']	{'title': {'type': 'str', '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...	True	True	['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....	True	False	['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...	True	False	['model', 'dataset']	{'start_date': {'type': 'Optional', 'default': None}, 'end_date': {'type': 'Optional', '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...	True	False	['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...	True	False	['dataset', 'model']	{'shocks': {'type': 'List', 'default': [0.1]}, 'transformation': {'type': 'Optional', '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....	False	True	['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...	True	False	['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,...	True	False	['dataset']	{'title': {'type': 'str', 'default': 'Histogram of Scores'}, 'score_column': {'type': 'str', '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....	True	True	['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....	False	True	['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....	True	True	['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....	False	True	['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....	False	True	['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....	True	False	['datasets', 'model']	{}	['visualization', 'credit_risk']	['classification']
validmind.ongoing_monitoring.FeatureDrift	Feature Drift	Evaluates changes in feature distribution over time to identify potential model drift....	True	True	['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...	True	False	['datasets', 'model']	{}	['visualization']	['monitoring']
validmind.ongoing_monitoring.PredictionCorrelation	Prediction Correlation	Assesses correlation changes between model predictions from reference and monitoring datasets to detect potential...	True	True	['datasets', 'model']	{'drift_pct_threshold': {'type': 'float', 'default': 20}}	['visualization']	['monitoring']
validmind.ongoing_monitoring.PredictionProbabilitiesHistogramDrift	Prediction Probabilities Histogram Drift	Compares prediction probability distributions between reference and monitoring datasets....	True	True	['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...	True	False	['datasets', 'model']	{}	['visualization']	['monitoring']
validmind.ongoing_monitoring.ROCCurveDrift	ROC Curve Drift	Compares ROC curves between reference and monitoring datasets....	True	False	['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....	False	True	['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....	True	True	['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...	True	True	['datasets', 'model']	{'drift_pct_threshold': {'type': 'float', 'default': 20}}	['visualization']	['monitoring']
validmind.plots.BoxPlot	Box Plot	Generates customizable box plots for numerical features in a dataset with optional grouping using Plotly....	True	False	['dataset']	{'columns': {'type': 'Optional', 'default': None}, 'group_by': {'type': 'Optional', 'default': None}, 'width': {'type': 'int', 'default': 1200}, 'height': {'type': 'int', 'default': 600}, 'colors': {'type': 'Optional', 'default': None}, 'show_outliers': {'type': 'bool', 'default': True}, 'title_prefix': {'type': 'str', 'default': 'Box Plot of'}}	['tabular_data', 'visualization', 'data_quality']	['classification', 'regression', 'clustering']
validmind.plots.CorrelationHeatmap	Correlation Heatmap	Generates customizable correlation heatmap plots for numerical features in a dataset using Plotly....	True	False	['dataset']	{'columns': {'type': 'Optional', 'default': None}, 'method': {'type': 'str', 'default': 'pearson'}, 'show_values': {'type': 'bool', 'default': True}, 'colorscale': {'type': 'str', 'default': 'RdBu'}, 'width': {'type': 'int', 'default': 800}, 'height': {'type': 'int', 'default': 600}, 'mask_upper': {'type': 'bool', 'default': False}, 'threshold': {'type': 'Optional', 'default': None}, 'title': {'type': 'str', 'default': 'Correlation Heatmap'}}	['tabular_data', 'visualization', 'correlation']	['classification', 'regression', 'clustering']
validmind.plots.HistogramPlot	Histogram Plot	Generates customizable histogram plots for numerical features in a dataset using Plotly....	True	False	['dataset']	{'columns': {'type': 'Optional', 'default': None}, 'bins': {'type': 'Union', 'default': 30}, 'color': {'type': 'str', 'default': 'steelblue'}, 'opacity': {'type': 'float', 'default': 0.7}, 'show_kde': {'type': 'bool', 'default': True}, 'normalize': {'type': 'bool', 'default': False}, 'log_scale': {'type': 'bool', 'default': False}, 'title_prefix': {'type': 'str', 'default': 'Histogram of'}, 'width': {'type': 'int', 'default': 1200}, 'height': {'type': 'int', 'default': 800}, 'n_cols': {'type': 'int', 'default': 2}, 'vertical_spacing': {'type': 'float', 'default': 0.15}, 'horizontal_spacing': {'type': 'float', 'default': 0.1}}	['tabular_data', 'visualization', 'data_quality']	['classification', 'regression', 'clustering']
validmind.plots.ViolinPlot	Violin Plot	Generates interactive violin plots for numerical features using Plotly....	False	False	['dataset']	{'columns': {'type': 'Optional', 'default': None}, 'group_by': {'type': 'Optional', 'default': None}, 'width': {'type': 'int', 'default': 800}, 'height': {'type': 'int', 'default': 600}}	['tabular_data', 'visualization', 'distribution']	['classification', 'regression', 'clustering']
validmind.prompt_validation.Bias	Bias	Assesses potential bias in a Large Language Model by analyzing the distribution and order of exemplars in the...	False	True	['model']	{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}	['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....	False	True	['model']	{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}	['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....	False	True	['model']	{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}	['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....	False	True	['model']	{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}	['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....	False	True	['model']	{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}	['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...	False	True	['model', 'dataset']	{'num_tests': {'type': '_empty', 'default': 10}, 'judge_llm': {'type': '_empty', 'default': None}}	['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,...	False	True	['model']	{'min_threshold': {'type': '_empty', 'default': 7}, 'judge_llm': {'type': '_empty', 'default': None}}	['llm', 'zero_shot', 'few_shot']	['text_classification', 'text_summarization']
validmind.stats.CorrelationAnalysis	Correlation Analysis	Performs comprehensive correlation analysis with significance testing for numerical features....	False	True	['dataset']	{'columns': {'type': 'Optional', 'default': None}, 'method': {'type': 'str', 'default': 'pearson'}, 'significance_level': {'type': 'float', 'default': 0.05}, 'min_correlation': {'type': 'float', 'default': 0.1}}	['tabular_data', 'statistics', 'correlation']	['classification', 'regression', 'clustering']
validmind.stats.DescriptiveStats	Descriptive Stats	Provides comprehensive descriptive statistics for numerical features in a dataset....	False	True	['dataset']	{'columns': {'type': 'Optional', 'default': None}, 'include_advanced': {'type': 'bool', 'default': True}, 'confidence_level': {'type': 'float', 'default': 0.95}}	['tabular_data', 'statistics', 'data_quality']	['classification', 'regression', 'clustering']
validmind.stats.NormalityTests	Normality Tests	Performs multiple normality tests on numerical features to assess distribution normality....	False	True	['dataset']	{'columns': {'type': 'Optional', 'default': None}, 'alpha': {'type': 'float', 'default': 0.05}, 'tests': {'type': 'List', 'default': ['shapiro', 'anderson', 'kstest']}}	['tabular_data', 'statistics', 'normality']	['classification', 'regression', 'clustering']
validmind.stats.OutlierDetection	Outlier Detection	Detects outliers in numerical features using multiple statistical methods....	False	True	['dataset']	{'columns': {'type': 'Optional', 'default': None}, 'methods': {'type': 'List', 'default': ['iqr', 'zscore', 'isolation_forest']}, 'iqr_threshold': {'type': 'float', 'default': 1.5}, 'zscore_threshold': {'type': 'float', 'default': 3.0}, 'contamination': {'type': 'float', 'default': 0.1}}	['tabular_data', 'statistics', 'outliers']	['classification', 'regression', 'clustering']
validmind.unit_metrics.classification.Accuracy	Accuracy	Calculates the accuracy of a model	False	False	['dataset', 'model']	{}	['classification']	['classification']
validmind.unit_metrics.classification.F1	F1	Calculates the F1 score for a classification model.	False	False	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.Precision	Precision	Calculates the precision for a classification model.	False	False	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.ROC_AUC	ROC AUC	Calculates the ROC AUC for a classification model.	False	False	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.Recall	Recall	Calculates the recall for a classification model.	False	False	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.individual.AbsoluteError	Absolute Error	Calculates the absolute error per row for a classification model....	False	True	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.individual.BrierScore	Brier Score	Calculates the Brier score per row for a classification model....	False	True	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.individual.CalibrationError	Calibration Error	Calculates the calibration error per row for a classification model....	False	True	['model', 'dataset']	{'n_bins': {'type': 'int', 'default': 10}}	['classification']	['classification']
validmind.unit_metrics.classification.individual.ClassBalance	Class Balance	Calculates the class balance score per row for a classification model....	False	True	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.individual.Confidence	Confidence	Calculates the prediction confidence per row for a classification model....	False	True	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.individual.Correctness	Correctness	Calculates the correctness per row for a classification model....	False	True	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.individual.LogLoss	Log Loss	Calculates the logarithmic loss per row for a classification model....	False	True	['model', 'dataset']	{'eps': {'type': 'float', 'default': 1e-15}}	['classification']	['classification']
validmind.unit_metrics.classification.individual.OutlierScore	Outlier Score	Calculates the outlier score per row for a classification model....	False	True	['model', 'dataset']	{'contamination': {'type': 'float', 'default': 0.1}}	['classification']	['classification']
validmind.unit_metrics.classification.individual.ProbabilityError	Probability Error	Calculates the probability error per row for a classification model....	False	True	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.classification.individual.Uncertainty	Uncertainty	Calculates the prediction uncertainty per row for a classification model....	False	True	['model', 'dataset']	{}	['classification']	['classification']
validmind.unit_metrics.regression.AdjustedRSquaredScore	Adjusted R Squared Score	Calculates the adjusted R-squared score for a regression model.	False	False	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.GiniCoefficient	Gini Coefficient	Calculates the Gini coefficient for a regression model.	False	False	['dataset', 'model']	{}	['regression']	['regression']
validmind.unit_metrics.regression.HuberLoss	Huber Loss	Calculates the Huber loss for a regression model.	False	False	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.KolmogorovSmirnovStatistic	Kolmogorov Smirnov Statistic	Calculates the Kolmogorov-Smirnov statistic for a regression model.	False	False	['dataset', 'model']	{}	['regression']	['regression']
validmind.unit_metrics.regression.MeanAbsoluteError	Mean Absolute Error	Calculates the mean absolute error for a regression model.	False	False	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.MeanAbsolutePercentageError	Mean Absolute Percentage Error	Calculates the mean absolute percentage error for a regression model.	False	False	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.MeanBiasDeviation	Mean Bias Deviation	Calculates the mean bias deviation for a regression model.	False	False	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.MeanSquaredError	Mean Squared Error	Calculates the mean squared error for a regression model.	False	False	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.QuantileLoss	Quantile Loss	Calculates the quantile loss for a regression model.	False	False	['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.	False	False	['model', 'dataset']	{}	['regression']	['regression']
validmind.unit_metrics.regression.RootMeanSquaredError	Root Mean Squared Error	Calculates the root mean squared error for a regression model.	False	False	['model', 'dataset']	{}	['regression']	['regression']

Upgrade ValidMind

After installing ValidMind, you’ll want to periodically make sure you are on the latest version to access any new features and other enhancements.

Retrieve the information for the currently installed version of ValidMind:

%pip show validmind

Name: validmind
Version: 2.9.3
Summary: ValidMind Library
Home-page: 
Author: Andres Rodriguez
Author-email: andres@validmind.ai
License: DUAL LICENSE NOTICE

 This software is dual-licensed under the GNU Affero General Public License 
 version 3 (AGPL-3.0) and the ValidMind Commercial License. Users may choose 
 to use the software under either of these licenses, subject to their 
 respective terms and conditions.

                      COMMERCIAL LICENSE INQUIRY

 If your organization has policies regarding the use of software licensed 
 under the GNU Affero General Public License, or if you are interested in 
 applications beyond the scope of open-source licenses, a commercial license 
 may be more appropriate. The ValidMind Commercial License offers an 
 alternative to the AGPL, providing additional flexibility and benefits 
 suited for commercial use.

 For organizations looking for a license that allows for proprietary 
 development, customization, or other uses not covered under the AGPL, the 
 ValidMind Commercial License is designed to meet these needs. This license 
 is particularly beneficial for those seeking to integrate ValidMind software 
 into their own products or services without the requirement to disclose 
 proprietary source code.

 For inquiries regarding the licensing of this software, please contact 
 ValidMind at info@validmind.com.

                     GNU AFFERO GENERAL PUBLIC LICENSE
                        Version 3, 19 November 2007

  Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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                      END OF TERMS AND CONDITIONS

             How to Apply These Terms to Your New Programs

   If you develop a new program, and you want it to be of the greatest
 possible use to the public, the best way to achieve this is to make it
 free software which everyone can redistribute and change under these terms.

   To do so, attach the following notices to the program.  It is safest
 to attach them to the start of each source file to most effectively
 state the exclusion of warranty; and each file should have at least
 the "copyright" line and a pointer to where the full notice is found.

     <one line to give the program's name and a brief idea of what it does.>
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     This program is free software: you can redistribute it and/or modify
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     (at your option) any later version.

     This program is distributed in the hope that it will be useful,
     but WITHOUT ANY WARRANTY; without even the implied warranty of
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     You should have received a copy of the GNU Affero General Public License
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 Also add information on how to contact you by electronic and paper mail.

   If your software can interact with users remotely through a computer
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 get its source.  For example, if your program is a web application, its
 interface could display a "Source" link that leads users to an archive
 of the code.  There are many ways you could offer source, and different
 solutions will be better for different programs; see section 13 for the
 specific requirements.

   You should also get your employer (if you work as a programmer) or school,
 if any, to sign a "copyright disclaimer" for the program, if necessary.
 For more information on this, and how to apply and follow the GNU AGPL, see
 <https://www.gnu.org/licenses/>.
Location: /opt/hostedtoolcache/Python/3.11.13/x64/lib/python3.11/site-packages
Requires: aiohttp, anywidget, beautifulsoup4, ipywidgets, kaleido, matplotlib, mistune, nest-asyncio, openai, pandas, plotly, polars, python-dotenv, scikit-learn, seaborn, sentry-sdk, tabulate, tiktoken, tqdm
Required-by: 
Note: you may need to restart the kernel to use updated packages.

If the version returned is lower than the version indicated in our production open-source code, restart your notebook and run:

%pip install --upgrade validmind

You may need to restart your kernel after running the upgrade package for changes to be applied.

In summary

In this first notebook, you learned how to:

• Register a model within the ValidMind Platform and assign yourself as the validator
• Install and initialize the ValidMind Library
• Preview the validation report template for your model
• Explore the available tests offered by the ValidMind Library

Next steps

Start the model validation process

Now that the ValidMind Library is connected to your model in the ValidMind Library with the correct template applied, we can go ahead and start the model validation process: 2 — Start the model validation process