Principle:Interpretml Interpret Data Exploration
Metadata
| Field | Value |
|---|---|
| Sources | Interpretml_Interpret |
| Domains | Data_Analysis, Visualization |
| Updated | 2026-02-07 |
Overview
Data exploration explainers produce interactive visualizations of feature distributions and feature-response relationships for exploratory data analysis before model training.
Description
ClassHistogram and Marginal are data-level explainer classes that conform to the InterpretML explainer API. Marginal generates scatter plots of feature values versus response values along with density histograms, computing Pearson correlation for continuous features and box plots for categorical features. ClassHistogram generates stacked histogram visualizations showing how each feature's distribution differs across target classes. Both produce Plotly-based interactive figures through their corresponding explanation objects.
Usage
Use data exploration explainers for exploratory data analysis before model training to understand feature distributions, feature-response relationships, and class-conditional distributions.