📄️ Partitioned Models
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
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
Custom transformers allow you to create specialized preprocessing components that integrate seamlessly with xplainable models. Build domain-specific transformations while maintaining full transparency.
📄️ XEvolutionaryNetwork
XEvolutionaryNetwork is a sophisticated multi-layer optimization framework that acts like a "neural network for hyperparameter optimization." It chains together optimization layers to create powerful, automated machine learning pipelines.