About MLExplainer
=================

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

Overview
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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
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* **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
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* Binary Classification Models
* Multilabel Classification Models
* Any model compatible with SHAP explainers

Design Philosophy
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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