Getting Started with treemind

treemind is designed for analyzing gradient boosting models. It simplifies understanding how features influence predictions within specific intervals and provides powerful tools for analyzing individual features and their interactions.

Installation

Install treemind via pip:

pip install treemind

Key Features

  1. Feature Analysis: Provides statistical analysis on how features behave across different decision splits.

  2. Interaction Analysis: Identifies complex relationships between features by analyzing how they work together to influence predictions. The algorithm can analyze interactions up to n features, depending on memory constraints and time limitations.

  3. High Performance: Optimized with Cython for fast execution, even on large models and datasets.

  4. Advanced Visualization: Offers user-friendly plots to visually explain the model’s decision-making process and feature interactions.

  5. Compatibility with Popular Frameworks: Fully compatible with xgboost, lightgbm and catboost, supporting regression and binary classification tasks.