ValidMind for validation 2 — Start the validation process

Learn how to use ValidMind for your end-to-end validation process with our series of four introductory notebooks. In this second notebook, independently verify the data quality tests performed on the dataset used to train the champion.

You'll learn how to run relevant validation tests with ValidMind, log the results of those tests to the ValidMind Platform, and insert your logged test results as evidence into your validation report. You'll become familiar with the tests available in ValidMind, as well as how to run them. Running tests during validation is crucial to the effective challenge process, as we want to independently evaluate the evidence and assessments provided by the development team.

While running our tests in this notebook, we'll focus on:

• Ensuring that data used for training and testing the champion is of appropriate data quality
• Ensuring that the raw data has been preprocessed appropriately and that the resulting final datasets reflects this

For a full list of out-of-the-box tests and descriptions, use the interactive ValidMind test sandbox.

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

Prerequisites

In order to independently assess the quality of your datasets with notebook, you'll need to first have:

• Registered a model within the ValidMind Platform and granted yourself access to the model as a validator
• Installed the ValidMind Library in your local environment, allowing you to access all its features

Need help with the above steps?

Refer to the first notebook in this series: 1 — Set up the ValidMind Library for validation

Setting up

Initialize the ValidMind Library

First, let's connect up the ValidMind Library to our model we previously registered in the ValidMind Platform:

1. On the left sidebar that appears for your model, select Getting Started and select Validation from the Document drop-down menu.

2. Click Copy snippet to clipboard.

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

# Make sure the ValidMind Library is installed

%pip install -q validmind

# 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="...",
    document="validation-report",
)

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

2026-10-02 20:36:36,965 - INFO(validmind.api_client): 🎉 Connected to ValidMind!
📊 Model: [ValidMind Academy] Model validation (ID: cmalguc9y02ok199q2db381ib)
📁 Document Type: validation_report

Load the sample dataset

Let's first import the public Bank Customer Churn Prediction dataset from Kaggle, which was used to develop the dummy champion.

We'll use this dataset to review steps that should have been conducted during the initial development and documentation of the champion to ensure that the model was built correctly. By independently performing steps taken by the development team, we can confirm whether the model was built using appropriate and properly processed data.

In our below example, note that:

• The target column, Exited has a value of 1 when a customer has churned and 0 otherwise.
• The ValidMind Library provides a wrapper to automatically load the dataset as a Pandas DataFrame object. A Pandas Dataframe is a two-dimensional tabular data structure that makes use of rows and columns.

from validmind.datasets.classification import customer_churn as demo_dataset

print(
    f"Loaded demo dataset with: \n\n\t• Target column: '{demo_dataset.target_column}' \n\t• Class labels: {demo_dataset.class_labels}"
)

raw_df = demo_dataset.load_data()
raw_df.head()

Loaded demo dataset with: 

    • Target column: 'Exited' 
    • Class labels: {'0': 'Did not exit', '1': 'Exited'}

	CreditScore	Geography	Gender	Age	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
0	619	France	Female	42	2	0.00	1	1	1	101348.88	1
1	608	Spain	Female	41	1	83807.86	1	0	1	112542.58	0
2	502	France	Female	42	8	159660.80	3	1	0	113931.57	1
3	699	France	Female	39	1	0.00	2	0	0	93826.63	0
4	850	Spain	Female	43	2	125510.82	1	1	1	79084.10	0

Verifying data quality adjustments

Let's say that thanks to the documentation submitted by the development team (Learn more: ValidMind for development), we know that the sample dataset was first modified before being used to train the champion. After performing some data quality assessments on the raw dataset, it was determined that the dataset required rebalancing, and highly correlated features were also removed.

Identify qualitative tests

During validation, we use the same data processing logic and training procedure to confirm that the model's results can be reproduced independently, so let's start by doing some data quality assessments by running a few individual tests just like the development team did.

Use the vm.tests.list_tests() function introduced by the first notebook in this series in combination with vm.tests.list_tags() and vm.tests.list_tasks() to find which prebuilt tests are relevant for data quality assessment:

• tasks represent the kind of modeling task associated with a test. Here we'll focus on classification tasks.
• tags are free-form descriptions providing more details about the test, for example, what category the test falls into. Here we'll focus on the data_quality tag.

# Get the list of available task types
sorted(vm.tests.list_tasks())

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

# Get the list of available tags
sorted(vm.tests.list_tags())

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

You can pass tags and tasks as parameters to the vm.tests.list_tests() function to filter the tests based on the tags and task types.

For example, to find tests related to tabular data quality for classification models, you can call list_tests() like this:

vm.tests.list_tests(task="classification", tags=["tabular_data", "data_quality"])

ID	Name	Description	Has Figure	Has Table	Required Inputs	Params	Tags	Tasks
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.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.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.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.MissingValues	Missing Values	Evaluates dataset quality by ensuring missing value percentage across all features does not exceed a set threshold....	False	True	['dataset']	{'min_percentage_threshold': {'type': 'float', 'default': 1.0}}	['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.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.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': 1800}, 'height': {'type': 'int', 'default': 1200}, '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.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.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']

Want to learn more about navigating ValidMind tests?

Refer to our notebook outlining the utilities available for viewing and understanding available ValidMind tests: Explore tests

Initialize the ValidMind dataset

With the individual tests we want to run identified, the next step is to connect your data with a ValidMind Dataset object. This step is always necessary every time you want to connect a dataset to documentation and produce test results through ValidMind, but you only need to do it once per dataset.

Initialize a ValidMind dataset object using the init_dataset function from the ValidMind (vm) module. For this example, we'll pass in the following arguments:

• dataset — The raw dataset that you want to provide as input to tests.
• input_id — A unique identifier that allows tracking what inputs are used when running each individual test.
• target_column — A required argument if tests require access to true values. This is the name of the target column in the dataset.

# vm_raw_dataset is now a VMDataset object that you can pass to any ValidMind test
vm_raw_dataset = vm.init_dataset(
    dataset=raw_df,
    input_id="raw_dataset",
    target_column="Exited",
)

Run data quality tests

Now that we know how to initialize a ValidMind dataset object, we're ready to run some tests!

You run individual tests by calling the run_test function provided by the validmind.tests module. For the examples below, we'll pass in the following arguments:

• test_id — The ID of the test to run, as seen in the ID column when you run list_tests.
• params — A dictionary of parameters for the test. These will override any default_params set in the test definition.

Run tabular data tests

The inputs expected by a test can also be found in the test definition — let's take validmind.data_validation.DescriptiveStatistics as an example.

Note that the output of the describe_test() function below shows that this test expects a dataset as input:

vm.tests.describe_test("validmind.data_validation.DescriptiveStatistics")

▶ Test: Descriptive Statistics ('validmind.data_validation.DescriptiveStatistics')

Descriptive Statistics

Performs a detailed descriptive statistical analysis of both numerical and categorical data within a model's dataset.

Purpose

The purpose of the Descriptive Statistics metric is to provide a comprehensive summary of both numerical and categorical data within a dataset. This involves statistics such as count, mean, standard deviation, minimum and maximum values for numerical data. For categorical data, it calculates the count, number of unique values, most common value and its frequency, and the proportion of the most frequent value relative to the total. The goal is to visualize the overall distribution of the variables in the dataset, aiding in understanding the model's behavior and predicting its performance.

Test Mechanism

The testing mechanism utilizes two in-built functions of pandas dataframes: describe() for numerical fields and value_counts() for categorical fields. The describe() function pulls out several summary statistics, while value_counts() accounts for unique values. The resulting data is formatted into two distinct tables, one for numerical and another for categorical variable summaries. These tables provide a clear summary of the main characteristics of the variables, which can be instrumental in assessing the model's performance.

Signs of High Risk

• Skewed data or significant outliers can represent high risk. For numerical data, this may be reflected via a significant difference between the mean and median (50% percentile).
• For categorical data, a lack of diversity (low count of unique values), or overdominance of a single category (high frequency of the top value) can indicate high risk.

Strengths

• Provides a comprehensive summary of the dataset, shedding light on the distribution and characteristics of the variables under consideration.
• It is a versatile and robust method, applicable to both numerical and categorical data.
• Helps highlight crucial anomalies such as outliers, extreme skewness, or lack of diversity, which are vital in understanding model behavior during testing and validation.

Limitations

• While this metric offers a high-level overview of the data, it may fail to detect subtle correlations or complex patterns.
• Does not offer any insights on the relationship between variables.
• Alone, descriptive statistics cannot be used to infer properties about future unseen data.
• Should be used in conjunction with other statistical tests to provide a comprehensive understanding of the model's data.

Required Inputs: dataset

Parameters:

Parameter	Default Value

How to Run:

Code:

        
import validmind as vm

# inputs dictionary maps your inputs to the expected input names
# keys are the expected input names and values are the actual inputs
# values may be string input_ids or the actual VMDataset or VMModel objects
inputs = {
    "dataset": "my_vm_dataset"
}
params = {}

# to run and view the result of this test, run the following code:
result = vm.tests.run_test(
  "validmind.data_validation.DescriptiveStatistics", inputs=inputs, params=params
)

# To see the result of the test, ensure that you have called `vm.init()` and then run:
result.log()
    

Now, let's run a few tests to assess the quality of the dataset:

result2 = vm.tests.run_test(
    test_id="validmind.data_validation.ClassImbalance",
    inputs={"dataset": vm_raw_dataset},
    params={"min_percent_threshold": 30},
)

❌ Class Imbalance

The Class Imbalance test evaluates the distribution of target classes in the dataset by comparing each class share against a minimum percentage threshold. In this run, the threshold was set to 30%, and the target variable Exited contains two classes. The results table and accompanying bar chart show that class Exited = 0 represents 79.80% of rows, while class Exited = 1 represents 20.20% of rows. The test assigns a pass/fail outcome to each class based on whether its observed share exceeds the configured threshold.

Key insights:

• Majority class dominates distribution: Exited = 0 accounts for 79.80% of observations, making it the dominant class in the target distribution.
• Minority class falls below threshold: Exited = 1 represents 20.20% of rows, which is below the configured 30% minimum threshold and is therefore marked as Fail.
• Pass/fail outcome differs by class: The class-level assessment is mixed, with Exited = 0 marked as Pass and Exited = 1 marked as Fail under the applied threshold.

The results show a two-class target distribution with a pronounced difference in class representation. Most observations are concentrated in Exited = 0, while Exited = 1 remains below the configured minimum percentage threshold. Under the parameters used in this test, the observed class distribution is not balanced across both target classes.

Parameters:

{
  "min_percent_threshold": 30
}
            

Tables

Exited Class Imbalance

Exited	Percentage of Rows (%)	Pass/Fail
0	79.80%	Pass
1	20.20%	Fail

Figures

The output above shows that the validmind.data_validation.ClassImbalance test did not pass according to the value we set for min_percent_threshold — great, this matches what was reported by the development team.

To address this issue, we'll re-run the test on some processed data. In this case let's apply a very simple rebalancing technique to the dataset:

import pandas as pd

raw_copy_df = raw_df.sample(frac=1)  # Create a copy of the raw dataset

# Create a balanced dataset with the same number of exited and not exited customers
exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 1]
not_exited_df = raw_copy_df.loc[raw_copy_df["Exited"] == 0].sample(n=exited_df.shape[0])

balanced_raw_df = pd.concat([exited_df, not_exited_df])
balanced_raw_df = balanced_raw_df.sample(frac=1, random_state=42)

With this new balanced dataset, you can re-run the individual test to see if it now passes the class imbalance test requirement.

As this is technically a different dataset, remember to first initialize a new ValidMind Dataset object to pass in as input as required by run_test():

# Register new data and now 'balanced_raw_dataset' is the new dataset object of interest
vm_balanced_raw_dataset = vm.init_dataset(
    dataset=balanced_raw_df,
    input_id="balanced_raw_dataset",
    target_column="Exited",
)

# Pass the initialized `balanced_raw_dataset` as input into the test run
result = vm.tests.run_test(
    test_id="validmind.data_validation.ClassImbalance",
    inputs={"dataset": vm_balanced_raw_dataset},
    params={"min_percent_threshold": 30},
)

✅ Class Imbalance

The Class Imbalance test evaluates the distribution of target classes in the dataset by measuring the share of records in each class against a minimum percentage threshold. For the Exited target, the results table and accompanying bar chart show two classes, 0 and 1, each representing 50.00% of rows. The applied minimum threshold is 30%, and the output records a pass/fail assessment for each class.

Key insights:

• Equal class distribution: Both Exited = 0 and Exited = 1 account for 50.00% of observations, indicating an even split between the two target classes.
• All classes exceed threshold: Each class is above the configured 30% minimum percentage threshold, and both classes are marked as Pass.
• No observed under-represented class: The results do not show any target class with a lower frequency than the threshold used in this test.

The test results show a balanced target distribution for Exited, with identical class shares across the two observed outcomes. Under the specified 30% threshold, no class falls below the minimum proportion, and all evaluated classes pass the test. This indicates that the dataset used in this assessment does not exhibit class imbalance based on the reported class frequencies.

Parameters:

{
  "min_percent_threshold": 30
}
            

Tables

Exited Class Imbalance

Exited	Percentage of Rows (%)	Pass/Fail
0	50.00%	Pass
1	50.00%	Pass

Figures

Remove highly correlated features

Next, let's also remove highly correlated features from our dataset as outlined by the development team. Removing highly correlated features helps make the model simpler, more stable, and easier to understand.

You can utilize the output from a ValidMind test for further use — in this below example, to retrieve the list of features with the highest correlation coefficients and use them to reduce the final list of features for modeling.

First, we'll run validmind.data_validation.HighPearsonCorrelation with the balanced_raw_dataset we initialized previously as input as is for comparison with later runs:

corr_result = vm.tests.run_test(
    test_id="validmind.data_validation.HighPearsonCorrelation",
    params={"max_threshold": 0.3},
    inputs={"dataset": vm_balanced_raw_dataset},
)

❌ High Pearson Correlation

The High Pearson Correlation test evaluates pairwise linear relationships between features to identify potentially redundant or highly collinear variable combinations. The results table lists the top correlation pairs, their Pearson coefficients, and a Pass/Fail flag based on the configured absolute threshold of 0.3. In this run, the reported coefficients range from -0.2015 to 0.3195 across the top ten pairs. Only one pair exceeds the threshold and is marked as Fail, while the remaining nine pairs are marked as Pass.

Key insights:

• One pair exceeds threshold: The pair (Age, Exited) has a Pearson correlation of 0.3195, which is the only reported coefficient above the 0.3 threshold and is therefore flagged as Fail.
• Remaining reported correlations are low: The other nine reported pairs all fall within -0.2015 to 0.1426 in absolute value, remaining below the configured threshold and receiving Pass status.
• Strongest negative relationship is modest: The most negative reported coefficient is -0.2015 for (IsActiveMember, Exited), which remains below the threshold and is classified as Pass.
• Top reported pairs are concentrated near zero: Aside from (Age, Exited), the listed correlations are relatively small in magnitude, including values such as -0.1798 for (Balance, NumOfProducts) and -0.0633 for (NumOfProducts, Exited).

The reported correlation structure is characterized by a single threshold breach and otherwise low-magnitude pairwise linear relationships among the top listed combinations. The most prominent observed relationship is the positive correlation between Age and Exited, while all other reported pairs remain below the configured cutoff. Overall, the table indicates limited evidence of high pairwise linear association within the reported top correlations.

Parameters:

{
  "max_threshold": 0.3
}
            

Tables

Columns	Coefficient	Pass/Fail
(Age, Exited)	0.3195	Fail
(IsActiveMember, Exited)	-0.2015	Pass
(Balance, NumOfProducts)	-0.1798	Pass
(Balance, Exited)	0.1426	Pass
(NumOfProducts, Exited)	-0.0633	Pass
(NumOfProducts, IsActiveMember)	0.0485	Pass
(Tenure, IsActiveMember)	-0.0429	Pass
(CreditScore, EstimatedSalary)	-0.0389	Pass
(Age, NumOfProducts)	-0.0374	Pass
(Balance, HasCrCard)	-0.0357	Pass

The output above shows that the test did not pass according to the value we set for max_threshold — as reported and expected.

corr_result is an object of type TestResult. We can inspect the result object to see what the test has produced:

print(type(corr_result))
print("Result ID: ", corr_result.result_id)
print("Params: ", corr_result.params)
print("Passed: ", corr_result.passed)
print("Tables: ", corr_result.tables)

<class 'validmind.vm_models.result.result.TestResult'>
Result ID:  validmind.data_validation.HighPearsonCorrelation
Params:  {'max_threshold': 0.3}
Passed:  False
Tables:  [ResultTable]

Let's remove the highly correlated features and create a new VM dataset object.

We'll begin by checking out the table in the result and extracting a list of features that failed the test:

# Extract table from `corr_result.tables`
features_df = corr_result.tables[0].data
features_df

	Columns	Coefficient	Pass/Fail
0	(Age, Exited)	0.3195	Fail
1	(IsActiveMember, Exited)	-0.2015	Pass
2	(Balance, NumOfProducts)	-0.1798	Pass
3	(Balance, Exited)	0.1426	Pass
4	(NumOfProducts, Exited)	-0.0633	Pass
5	(NumOfProducts, IsActiveMember)	0.0485	Pass
6	(Tenure, IsActiveMember)	-0.0429	Pass
7	(CreditScore, EstimatedSalary)	-0.0389	Pass
8	(Age, NumOfProducts)	-0.0374	Pass
9	(Balance, HasCrCard)	-0.0357	Pass

# Extract list of features that failed the test
high_correlation_features = features_df[features_df["Pass/Fail"] == "Fail"]["Columns"].tolist()
high_correlation_features

['(Age, Exited)']

Next, extract the feature names from the list of strings (example: (Age, Exited) > Age):

high_correlation_features = [feature.split(",")[0].strip("()") for feature in high_correlation_features]
high_correlation_features

['Age']

Now, it's time to re-initialize the dataset with the highly correlated features removed.

Note the use of a different input_id. This allows tracking the inputs used when running each individual test.

# Remove the highly correlated features from the dataset
balanced_raw_no_age_df = balanced_raw_df.drop(columns=high_correlation_features)

# Re-initialize the dataset object
vm_raw_dataset_preprocessed = vm.init_dataset(
    dataset=balanced_raw_no_age_df,
    input_id="raw_dataset_preprocessed",
    target_column="Exited",
)

Re-running the test with the reduced feature set should pass the test:

corr_result = vm.tests.run_test(
    test_id="validmind.data_validation.HighPearsonCorrelation",
    params={"max_threshold": 0.3},
    inputs={"dataset": vm_raw_dataset_preprocessed},
)

✅ High Pearson Correlation

The High Pearson Correlation test evaluates pairwise linear relationships among features to identify potentially redundant or highly collinear variable pairs. The result table reports the top 10 strongest Pearson correlations after removing duplicate and self-correlations, using an absolute correlation threshold of 0.3 for pass/fail classification. In this run, all reported feature pairs are marked Pass, and the observed coefficients range from -0.2015 to 0.1426. The largest absolute correlation appears between IsActiveMember and Exited at -0.2015.

Key insights:

• No correlations exceed threshold: All 10 reported feature pairs are classified as Pass under the configured threshold of 0.3, indicating that none of the strongest observed linear relationships exceeded the test limit.

• Largest relationship is modest: The highest absolute coefficient is -0.2015 for the pair (IsActiveMember, Exited), which remains below the threshold and represents the strongest linear association in the reported results.

• Top correlations are concentrated at low magnitudes: The next-largest absolute coefficients are -0.1798 for (Balance, NumOfProducts) and 0.1426 for (Balance, Exited). All remaining reported correlations have absolute values below 0.10.

• Reported relationships are mixed in direction: Both negative and positive coefficients are present in the top-ranked pairs, with the strongest relationships skewing negative, including (IsActiveMember, Exited) at -0.2015 and (Balance, NumOfProducts) at -0.1798.

The reported correlation structure shows that the strongest observed pairwise linear relationships remain below the configured 0.3 threshold. The largest associations are modest in magnitude, with only three reported pairs exceeding an absolute value of 0.10 and the remainder clustering closer to zero. Based on the reported top correlations, the dataset does not exhibit high pairwise Pearson correlation under this test configuration.

Parameters:

{
  "max_threshold": 0.3
}
            

Tables

Columns	Coefficient	Pass/Fail
(IsActiveMember, Exited)	-0.2015	Pass
(Balance, NumOfProducts)	-0.1798	Pass
(Balance, Exited)	0.1426	Pass
(NumOfProducts, Exited)	-0.0633	Pass
(NumOfProducts, IsActiveMember)	0.0485	Pass
(Tenure, IsActiveMember)	-0.0429	Pass
(CreditScore, EstimatedSalary)	-0.0389	Pass
(Balance, HasCrCard)	-0.0357	Pass
(CreditScore, IsActiveMember)	0.0349	Pass
(Tenure, HasCrCard)	0.0312	Pass

You can also plot the correlation matrix to visualize the new correlation between features:

corr_result = vm.tests.run_test(
    test_id="validmind.data_validation.PearsonCorrelationMatrix",
    inputs={"dataset": vm_raw_dataset_preprocessed},
)

Pearson Correlation Matrix

The PearsonCorrelationMatrix test evaluates linear dependency among numerical variables using pairwise Pearson correlation coefficients displayed in a heat map. The result shows correlations across CreditScore, Tenure, Balance, NumOfProducts, HasCrCard, IsActiveMember, EstimatedSalary, and Exited, with coefficients ranging from -0.20 to 0.14 outside the diagonal. Diagonal values are 1.0 by construction, and the off-diagonal cells are concentrated near zero, indicating generally weak linear relationships across the variables included in the matrix.

Key insights:

• No high pairwise correlations observed: All off-diagonal correlation coefficients are well below the stated 0.70 threshold in absolute value. The largest observed magnitudes are -0.20 and -0.18, indicating the matrix does not contain strongly correlated variable pairs.
• Exited shows weak associations: Exited has correlations of -0.20 with IsActiveMember, 0.14 with Balance, -0.06 with NumOfProducts, and values near zero with the remaining variables. These results indicate only weak linear relationships between Exited and the other numerical variables shown.
• Balance and NumOfProducts are mildly negatively related: The correlation between Balance and NumOfProducts is -0.18, which is one of the larger non-target relationships in the matrix. Despite being among the highest off-diagonal values, its magnitude remains modest.
• Most feature relationships are near zero: Many pairwise coefficients, including those involving CreditScore, Tenure, HasCrCard, and EstimatedSalary, fall between approximately -0.04 and 0.05. This pattern indicates limited linear dependence across most variable pairs in the dataset.

The correlation matrix shows a sparse linear dependency structure, with all observed off-diagonal correlations remaining low in magnitude. The most notable relationships are the weak negative correlation between Exited and IsActiveMember and the weak negative correlation between Balance and NumOfProducts, followed by a weak positive correlation between Exited and Balance. Overall, the result does not show evidence of strong pairwise linear redundancy among the numerical variables included in this test.

Figures

Documenting test results

Now that we've done some analysis on two different datasets, we can use ValidMind to easily document why certain things were done to our raw data with testing to support it. Every test result returned by the run_test() function has a .log() method that can be used to send the test results to the ValidMind Platform.

When logging validation test results to the platform, you'll need to manually add those results to the desired section of the validation report. To demonstrate how to add test results to your validation report, we'll log our data quality tests and insert the results via the ValidMind Platform.

Configure and run comparison tests

Below, we'll perform comparison tests between the original raw dataset (raw_dataset) and the final preprocessed (raw_dataset_preprocessed) dataset, again logging the results to the ValidMind Platform.

We can specify all the tests we'd ike to run in a dictionary called test_config, and we'll pass in the following arguments for each test:

• params: Individual test parameters.
• input_grid: Individual test inputs to compare. In this case, we'll input our two datasets for comparison.

Note here that the input_grid expects the input_id of the dataset as the value rather than the variable name we specified:

# Individual test config with inputs specified
test_config = {
    "validmind.data_validation.ClassImbalance": {
        "input_grid": {"dataset": ["raw_dataset", "raw_dataset_preprocessed"]},
        "params": {"min_percent_threshold": 30}
    },
    "validmind.data_validation.HighPearsonCorrelation": {
        "input_grid": {"dataset": ["raw_dataset", "raw_dataset_preprocessed"]},
        "params": {"max_threshold": 0.3}
    },
}

Then batch run and log our tests in test_config:

for t in test_config:
    print(t)
    try:
        # Check if test has input_grid
        if 'input_grid' in test_config[t]:
            # For tests with input_grid, pass the input_grid configuration
            if 'params' in test_config[t]:
                vm.tests.run_test(t, input_grid=test_config[t]['input_grid'], params=test_config[t]['params']).log()
            else:
                vm.tests.run_test(t, input_grid=test_config[t]['input_grid']).log()
        else:
            # Original logic for regular inputs
            if 'params' in test_config[t]:
                vm.tests.run_test(t, inputs=test_config[t]['inputs'], params=test_config[t]['params']).log()
            else:
                vm.tests.run_test(t, inputs=test_config[t]['inputs']).log()
    except Exception as e:
        print(f"Error running test {t}: {str(e)}")

validmind.data_validation.ClassImbalance

❌ Class Imbalance

The Class Imbalance test evaluates the distribution of target classes by measuring each class’s share of total records against the configured minimum threshold. In this run, results are reported for the Exited target across both raw_dataset and raw_dataset_preprocessed, using a minimum percentage threshold of 30%. The output table and accompanying bar charts show the class percentages for Exited = 0 and Exited = 1 in each dataset, together with the corresponding pass/fail outcome for each class.

Key insights:

• Raw dataset is imbalanced: In raw_dataset, Exited = 0 represents 79.80% of rows and passes the 30% threshold, while Exited = 1 represents 20.20% and fails. This indicates an uneven class distribution in the original dataset.

• Preprocessed dataset is balanced: In raw_dataset_preprocessed, both Exited = 0 and Exited = 1 account for 50.00% of rows. Both classes pass the 30% threshold.

• Minority class share increased materially: The proportion of Exited = 1 increases from 20.20% in raw_dataset to 50.00% in raw_dataset_preprocessed. This changes the class from below-threshold to above-threshold status.

• Pass/fail outcome changed after preprocessing: The test result shifts from one failing class in raw_dataset to no failing classes in raw_dataset_preprocessed. The change is visible in both the tabular results and the class proportion plots.

The results show that class imbalance is present in the original dataset but not in the preprocessed dataset under the 30% minimum class threshold. The original distribution is concentrated in Exited = 0, with Exited = 1 below threshold, whereas the preprocessed dataset shows an even 50/50 split across the two classes. Collectively, the test output documents a clear change in target class distribution between the raw and preprocessed versions of the data.

Parameters:

{
  "min_percent_threshold": 30
}
            

Tables

dataset	Exited	Percentage of Rows (%)	Pass/Fail
raw_dataset	0	79.80%	Pass
raw_dataset	1	20.20%	Fail
raw_dataset_preprocessed	0	50.00%	Pass
raw_dataset_preprocessed	1	50.00%	Pass

Figures

2026-10-02 20:37:34,901 - INFO(validmind.vm_models.result.result): Test driven block with result_id validmind.data_validation.ClassImbalance does not exist in model's document

validmind.data_validation.HighPearsonCorrelation

❌ High Pearson Correlation

The High Pearson Correlation test evaluates pairwise linear relationships between features to identify highly correlated variable pairs that may indicate redundancy or multicollinearity. The results report the top correlations for both raw_dataset and raw_dataset_preprocessed using a maximum threshold of 0.3, with each pair labeled as Pass or Fail based on whether the absolute coefficient exceeds that threshold. In the raw dataset, one pair exceeds the threshold and is flagged as Fail, while all other reported pairs in both datasets remain within the threshold. The reported coefficients span both positive and negative relationships, with the largest absolute values concentrated in a small number of feature pairs.

Key insights:

• One raw-data pair exceeds threshold: In raw_dataset, the pair (Balance, NumOfProducts) has a Pearson coefficient of -0.3045, which exceeds the absolute threshold of 0.3 and is marked as Fail.

• Preprocessing reduced the strongest correlation: The same pair, (Balance, NumOfProducts), declines from -0.3045 in raw_dataset to -0.1798 in raw_dataset_preprocessed, changing from Fail to Pass.

• Most reported relationships are weak: Aside from the single failed pair in the raw dataset, all listed coefficients in both datasets fall between -0.2015 and 0.2810, indicating relatively limited linear association among the reported top pairs.

• Target-related correlations remain below threshold: Among pairs involving Exited, the largest reported absolute coefficient is (Age, Exited) at 0.2810 in raw_dataset, followed by (IsActiveMember, Exited) at -0.2015 in raw_dataset_preprocessed; both remain within the configured limit.

The correlation results show a largely low-to-moderate linear dependence structure across the reported feature pairs, with only one threshold breach observed in the raw dataset. The most material change between datasets is the reduction in the (Balance, NumOfProducts) correlation after preprocessing, eliminating the only reported failure. Correlations involving Exited are among the larger reported relationships, but none exceed the configured threshold in either dataset.

Parameters:

{
  "max_threshold": 0.3
}
            

Tables

dataset	Columns	Coefficient	Pass/Fail
raw_dataset	(Balance, NumOfProducts)	-0.3045	Fail
raw_dataset	(Age, Exited)	0.2810	Pass
raw_dataset	(IsActiveMember, Exited)	-0.1515	Pass
raw_dataset	(Balance, Exited)	0.1174	Pass
raw_dataset	(Age, IsActiveMember)	0.0873	Pass
raw_dataset	(NumOfProducts, Exited)	-0.0523	Pass
raw_dataset	(Age, NumOfProducts)	-0.0306	Pass
raw_dataset	(CreditScore, IsActiveMember)	0.0306	Pass
raw_dataset	(Tenure, IsActiveMember)	-0.0293	Pass
raw_dataset	(Age, Balance)	0.0290	Pass
raw_dataset_preprocessed	(IsActiveMember, Exited)	-0.2015	Pass
raw_dataset_preprocessed	(Balance, NumOfProducts)	-0.1798	Pass
raw_dataset_preprocessed	(Balance, Exited)	0.1426	Pass
raw_dataset_preprocessed	(NumOfProducts, Exited)	-0.0633	Pass
raw_dataset_preprocessed	(NumOfProducts, IsActiveMember)	0.0485	Pass
raw_dataset_preprocessed	(Tenure, IsActiveMember)	-0.0429	Pass
raw_dataset_preprocessed	(CreditScore, EstimatedSalary)	-0.0389	Pass
raw_dataset_preprocessed	(Balance, HasCrCard)	-0.0357	Pass
raw_dataset_preprocessed	(CreditScore, IsActiveMember)	0.0349	Pass
raw_dataset_preprocessed	(Tenure, HasCrCard)	0.0312	Pass

2026-10-02 20:37:45,130 - INFO(validmind.vm_models.result.result): Test driven block with result_id validmind.data_validation.HighPearsonCorrelation does not exist in model's document

Note the output returned indicating that a test-driven block doesn't currently exist in your documentation for some test IDs.

That's expected, as when we run validations tests the results logged need to be manually added to your report as part of your compliance assessment process within the ValidMind Platform.

Log tests with unique identifiers

Next, we'll use the previously initialized vm_balanced_raw_dataset (that still has a highly correlated Age column) as input to run an individual test, then log the result to the ValidMind Platform.

When running individual tests, you can use a custom result_id to tag the individual result with a unique identifier:

• This result_id can be appended to test_id with a : separator.
• The balanced_raw_dataset result identifier will correspond to the balanced_raw_dataset input, the dataset that still has the Age column.

result = vm.tests.run_test(
    test_id="validmind.data_validation.HighPearsonCorrelation:balanced_raw_dataset",
    params={"max_threshold": 0.3},
    inputs={"dataset": vm_balanced_raw_dataset},
)
result.log()

❌ High Pearson Correlation Balanced Raw Dataset

The High Pearson Correlation test evaluates pairwise linear relationships among features to identify potentially redundant or strongly related variable pairs. The result table reports the top 10 correlations from the balanced raw dataset, showing each feature pair, its Pearson correlation coefficient, and whether it exceeds the configured absolute threshold of 0.3. Observed coefficients range from -0.2015 to 0.3195, with one pair marked as Fail and the remaining nine marked as Pass.

Key insights:

• One pair exceeds threshold: The pair (Age, Exited) has a correlation coefficient of 0.3195, which is the only reported value above the 0.3 threshold and is therefore marked Fail.
• Most relationships are weak: The remaining nine reported feature pairs all fall within -0.2015 to 0.1426 in coefficient magnitude, remaining below the configured threshold and marked Pass.
• Largest negative correlation remains below threshold: (IsActiveMember, Exited) shows the strongest negative relationship in the table at -0.2015, which does not breach the threshold.
• Reported correlations are concentrated near zero: Aside from (Age, Exited), all listed coefficients are relatively close to zero, including (Balance, NumOfProducts) at -0.1798 and (Balance, Exited) at 0.1426.

The reported correlation structure is limited to a single feature pair exceeding the configured threshold, with (Age, Exited) representing the strongest linear relationship in the table. All other listed relationships remain below the threshold and are comparatively small in magnitude. Taken together, the result indicates that high pairwise linear correlation is not widespread among the top reported feature pairs in this dataset.

Parameters:

{
  "max_threshold": 0.3
}
            

Tables

Columns	Coefficient	Pass/Fail
(Age, Exited)	0.3195	Fail
(IsActiveMember, Exited)	-0.2015	Pass
(Balance, NumOfProducts)	-0.1798	Pass
(Balance, Exited)	0.1426	Pass
(NumOfProducts, Exited)	-0.0633	Pass
(NumOfProducts, IsActiveMember)	0.0485	Pass
(Tenure, IsActiveMember)	-0.0429	Pass
(CreditScore, EstimatedSalary)	-0.0389	Pass
(Age, NumOfProducts)	-0.0374	Pass
(Balance, HasCrCard)	-0.0357	Pass

2026-10-02 20:37:51,627 - INFO(validmind.vm_models.result.result): Test driven block with result_id validmind.data_validation.HighPearsonCorrelation:balanced_raw_dataset does not exist in model's document

Add test results to reporting

With some test results logged, let's head to the model we connected to at the beginning of this notebook and learn how to insert a test result into our validation report. (Learn more: Assess compliance)

While the example below focuses on a specific test result, you can follow the same general procedure for your other results:

1. From the Inventory in the ValidMind Platform, go to the model you connected to earlier.

2. In the left sidebar that appears for your model, click Validation under Documents.

3. Click on 2.2.1. Data Quality to expand that section.

4. Under the Class Imbalance Assessment guideline, click Evidence to expand the evidence panel.

5. Click Link Evidence, then select Validator Evidence.

6. Select the Class Imbalance test results we logged: ValidMind Data Validation Class Imbalance

   
   
   

7. Click Update Linked Evidence to add the test results to the validation report.

8. Confirm that the results for the Class Imbalance test you inserted has been correctly inserted into section 2.2.1. Data Quality of the report.

   • Note that these test results are flagged as Requires Attention — as they include comparative results from our initial raw dataset.
   • Click See evidence details to review the LLM-generated description that summarizes the test results, that confirm that our final preprocessed dataset actually passes our test:

   
   
   

Here in this text editor, you can make qualitative edits to the draft that ValidMind generated to finalize the test results.

Learn more: Work with content blocks

Preparing the preprocessed dataset

Split the preprocessed dataset

With our raw dataset rebalanced with highly correlated features removed, let's now spilt our dataset into train and test in preparation for model evaluation testing.

To start, let's grab the first few rows from the balanced_raw_no_age_df dataset we initialized earlier:

balanced_raw_no_age_df.head()

	CreditScore	Geography	Gender	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited
6940	504	Spain	Male	0	54980.81	1	1	1	136909.88	0
4155	733	Germany	Male	5	125725.02	2	1	1	50783.10	0
5878	606	Spain	Male	10	0.00	2	1	0	177938.52	0
6746	567	France	Female	9	137891.35	1	1	0	142009.46	1
7543	596	Germany	Male	1	123544.00	1	1	1	120314.75	1

Before training the model, we need to encode the categorical features in the dataset:

• Use the OneHotEncoder class from the sklearn.preprocessing module to encode the categorical features.
• The categorical features in the dataset are Geography and Gender.

balanced_raw_no_age_df = pd.get_dummies(
    balanced_raw_no_age_df, columns=["Geography", "Gender"], drop_first=True
)
balanced_raw_no_age_df.head()

	CreditScore	Tenure	Balance	NumOfProducts	HasCrCard	IsActiveMember	EstimatedSalary	Exited	Geography_Germany	Geography_Spain	Gender_Male
6940	504	0	54980.81	1	1	1	136909.88	0	False	True	True
4155	733	5	125725.02	2	1	1	50783.10	0	True	False	True
5878	606	10	0.00	2	1	0	177938.52	0	False	True	True
6746	567	9	137891.35	1	1	0	142009.46	1	False	False	False
7543	596	1	123544.00	1	1	1	120314.75	1	True	False	True

Splitting our dataset into training and testing is essential for proper validation testing, as this helps assess how well the model generalizes to unseen data:

• We start by dividing our balanced_raw_no_age_df dataset into training and test subsets using train_test_split, with 80% of the data allocated to training (train_df) and 20% to testing (test_df).
• From each subset, we separate the features (all columns except "Exited") into X_train and X_test, and the target column ("Exited") into y_train and y_test.

from sklearn.model_selection import train_test_split

train_df, test_df = train_test_split(balanced_raw_no_age_df, test_size=0.20)

X_train = train_df.drop("Exited", axis=1)
y_train = train_df["Exited"]
X_test = test_df.drop("Exited", axis=1)
y_test = test_df["Exited"]

Initialize the split datasets

Next, let's initialize the training and testing datasets so they are available for use:

vm_train_ds = vm.init_dataset(
    input_id="train_dataset_final",
    dataset=train_df,
    target_column="Exited",
)

vm_test_ds = vm.init_dataset(
    input_id="test_dataset_final",
    dataset=test_df,
    target_column="Exited",
)

In summary

In this second notebook, you learned how to:

• Import a sample dataset
• Identify which tests you might want to run with ValidMind
• Initialize ValidMind datasets
• Run individual tests
• Utilize the output from tests you’ve run
• Log test results as evidence to the ValidMind Platform
• Insert test results into your validation report

Next steps

Develop potential challenger models

Now that you're familiar with the basics of using the ValidMind Library, let's use it to develop a challenger model: 3 — Developing a potential challenger

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