Implements a robust feature binning utility that converts continuous features into discrete bins using percentile or interval strategies. Handles missing values by assigning them to a dedicated bin, supports subsampling for large datasets, and provides a clean transformer interface compatible with scikit-learn. The component is isolated (depends only on numpy and sklearn utilities) and demonstrates non-obvious skill in efficient binning implementation with proper edge case handling.

Provides a consistent estimator wrapper that abstracts differences between custom and scikit-learn backend implementations of Random Forest and Extra Trees. Ensures uniform access to OOB predictions, feature importances, and prediction methods regardless of the underlying estimator type. Demonstrates the adapter pattern with clean separation of concerns and minimal coupling to the rest of the system.

Contains essential utility functions for the Deep Forest algorithm, including merge_proba for averaging probability outputs from multiple estimators, and array initialization/merging functions for managing augmented features across cascade layers. These functions are self-contained, well-documented, and showcase non-obvious skill in efficient numerical operations that are critical to the algorithm's performance.