Principle:Interpretml Interpret Linear Model Explanation
Metadata
| Field | Value |
|---|---|
| Sources | Interpretml_Interpret |
| Domains | Machine_Learning, Interpretability |
| Updated | 2026-02-07 |
Overview
Linear models provide inherently interpretable explanations through coefficient-based feature attribution for both regression and classification tasks.
Description
LinearRegression and LogisticRegression wrap scikit-learn's linear models to provide coefficient-based global and local explanations within the InterpretML framework. Global explanations show feature coefficients as importance measures, while local explanations decompose individual predictions into per-feature contributions by multiplying coefficients with feature values. These models are among the most interpretable machine learning methods since the contribution of each feature is directly readable from the model coefficients.
Usage
Use linear model explanations when you need a baseline interpretable model where each feature's contribution to the prediction is a simple linear (or log-linear for logistic regression) function of its value.