About MLExplainer

MLExplainer is an advanced machine learning explainability library designed for data scientists working with modern frameworks.

Overview

This library focuses on SHAP (SHapley Additive exPlanations) values for model interpretation, providing both global and local explanations for your machine learning models.

Key Features

  • SHAP Integration: Built-in support for SHAP explainers with optimized workflows

  • Multiple Classification Types: Support for binary and multilabel classification tasks

  • Automatic Feature Detection: Intelligent categorization of numerical, categorical, and string features

  • Rich Visualizations: Integrated plotting for feature-target relationships and SHAP value distributions

  • Validation Tools: Built-in interpretation consistency validation

  • Modern Architecture: Clean, extensible design with proper abstractions

Supported Model Types

  • Binary Classification Models

  • Multilabel Classification Models

  • Any model compatible with SHAP explainers

Design Philosophy

MLExplainer follows clean architecture principles with:

  • Template Method Pattern: Standardized explanation workflows

  • Strategy Pattern: Flexible plotting strategies for different feature types

  • Comprehensive Testing: Extensive test coverage for reliability

  • Type Safety: Full type hints and mypy compatibility