Partitioned models enable training separate transparent models on different data segments, then combining them for improved accuracy and deeper insights. Perfect for datasets with natural groupings or heterogeneous patterns.
Rapid refitting allows you to update model parameters in milliseconds without retraining from scratch. Perfect for real-time optimization, A/B testing, and parameter tuning scenarios.
Custom transformers allow you to write sklearn-compatible transformer classes and use them within xplainable-preprocessing pipelines. This enables domain-specific preprocessing while leveraging the full pipeline compilation and validation system.
XEvolutionaryNetwork is a layer-based optimization framework specifically designed for optimizing the leaf weights of XRegressor models. It chains together optimization layers (Tighten and Evolve) to iteratively improve model predictions.