ValidMind for model development 1 — Set up the ValidMind Library

Learn how to use ValidMind for your end-to-end model documentation process based on common model development scenarios with our series of four introductory notebooks. This first notebook walks you through the initial setup of the ValidMind Library.

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 developers new to ValidMind combines this series of notebooks with more a more in-depth introduction to the ValidMind Platform — Developer Fundamentals

Introduction

Model development aims to produce a fit-for-purpose champion model by conducting thorough testing and analysis, supporting the capabilities of the model with evidence in the form of documentation and test results. Model documentation should be clear and comprehensive, ideally following a structure or template covering all aspects of compliance with model risk regulation.

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 documentation and validation tests, and then use the ValidMind Platform to collaborate on model documentation. Together, these products simplify model risk management, facilitate compliance with regulations and institutional standards, and enhance collaboration between yourself and model validators.

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

Model documentation: A structured and detailed record pertaining to a model, encompassing key components such as its underlying assumptions, methodologies, data sources, inputs, performance metrics, evaluations, limitations, and intended uses. It serves to ensure transparency, adherence to regulatory requirements, and a clear understanding of potential risks associated with the model’s application.

Documentation template: Functions as a test suite and lays out the structure of model documentation, segmented into various sections and sub-sections. Documentation templates define the structure of your model documentation, specifying the tests that should be run, and how the results should be displayed.

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, and can be run individually or as part of a suite defined by your model documentation template.

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.

Test suites: Collections of tests designed to run together to automate and generate model documentation end-to-end for specific use-cases.

Example: the classifier_full_suite test suite runs tests from the tabular_dataset and classifier test suites to fully document the data and model sections for binary classification model use-cases.

Initializing the ValidMind Library

The ValidMind Library provides a rich collection of documentation tools and test suites, from documenting descriptions of datasets to validation and testing of models using a variety of open-source testing frameworks.

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 developer environment. You initialize the ValidMind Library with this code snippet, which ensures that your documentation and tests 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 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.

  4. 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 (2.8.28) than the latest version installed on your system (2.8.29). You may need to restart the kernel if you are experiencing issues.
2025-08-01 03:14:41,838 - INFO(validmind.api_client): 🎉 Connected to ValidMind!
📊 Model: [ValidMind Academy] Model development (ID: cmalgf3qi02ce199qm3rdkl46)
📁 Document Type: model_documentation

Getting to know ValidMind

Preview the documentation template

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

You will upload documentation and test results unique to your model based on this template 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 and note the empty sections:

vm.preview_template()

View model documentation 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 development" series of notebooks.

  3. Click Documents > Documentation for your model and note how the structure of the documentation matches our preview above.

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 learn how to run tests shortly.

You can see that the documentation template for this model has references to some of the test IDs used to run tests listed below:

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': {' ', 'dr', 'mrs', "'s", "s'", 'us', 's', 'dollar', "''", 'mr', 'ms', '``'}}, 'lang': {'type': 'str', 'default': 'english'}} ['nlp', 'text_data', 'visualization'] ['text_classification', 'text_summarization']
validmind.data_validation.nlp.Toxicity Toxicity Assesses the toxicity of text data within a dataset to visualize the distribution of toxicity scores.... 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.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.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.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: 2.8.29
Summary: ValidMind Library
Home-page: 
Author: Andres Rodriguez
Author-email: andres@validmind.ai
License: Commercial License
Location: /opt/hostedtoolcache/Python/3.11.13/x64/lib/python3.11/site-packages
Requires: aiohttp, anywidget, arch, bert-score, catboost, datasets, evaluate, h11, ipywidgets, kaleido, langdetect, matplotlib, mistune, nest-asyncio, nltk, numba, numpy, openai, pandas, plotly, plotly-express, polars, python-dotenv, rouge, scikit-learn, scipy, scorecardpy, seaborn, sentry-sdk, shap, statsmodels, tabulate, textblob, tiktoken, tqdm, xgboost, yfinance
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:

Next steps

Start the model development 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 development process: 2 — Start the model development process