Explainer Module

The Explainer module is designed to evaluate trained models by generating diagnostic plots, auditing bias, and producing comprehensive reports. It supports various supervised learning tasks, including classification, regression, and survival models.

The module provides an easy-to-use interface for model diagnostics, bias analysis, and feature importance visualization, facilitating deeper insights into the model's performance and fairness.

Features

Diagnostic Plots: Generates performance diagnostics, including classification metrics, regression plots, and SHAP value visualizations.
Bias Audit: Identifies potential biases in model predictions with respect to sensitive features.
Feature Importance: Calculates and visualizes feature importance using permutation importance or model-specific methods.
Comprehensive Reports: Creates a detailed PDF report summarizing all diagnostic results.

Example Usage

from jarvais.explainer import Explainer

# Prefered method is to initialize from trainer
exp = Explainer.from_trainer(trainer)
exp.run()

Output Files

The Explainer module generates the following files and directories:

explainer_report.pdf: A PDF report summarizing the model diagnostics, bias audit results, and feature importance.
bias/: Contains CSV files with bias metrics for different sensitive features.