Binning Methods

All available binning and discretization methods organized by their base classes.

Interval-Based Methods (Unsupervised)

These methods create interval bins through unsupervised analysis of the data distribution.

Supervised Methods

These methods use target variable information to create optimal bins for prediction tasks.

Flexible Methods

These methods allow for custom bin specifications and handle discrete values.

Quick Reference

EqualWidthBinning([n_bins, bin_range, clip, ...])

Equal width binning implementation for creating uniform interval bins.

EqualFrequencyBinning([n_bins, ...])

Equal frequency binning implementation for creating balanced population bins.

KMeansBinning([n_bins, allow_fallback, ...])

K-means clustering-based binning implementation for natural data groupings.

GaussianMixtureBinning([n_components, ...])

Gaussian Mixture Model clustering-based binning implementation using clean architecture.

DBSCANBinning([eps, min_samples, min_bins, ...])

DBSCAN clustering-based binning implementation using clean architecture.

EqualWidthMinimumWeightBinning([n_bins, ...])

Equal-width binning with minimum weight constraint implementation using clean architecture.

ManualIntervalBinning(bin_edges[, ...])

Manual interval binning implementation for user-defined bin boundaries.

TreeBinning([task_type, tree_params, clip, ...])

Tree-based supervised binning implementation using clean architecture.

Chi2Binning([max_bins, min_bins, alpha, ...])

Chi-square binning implementation for supervised discretization.

IsotonicBinning([max_bins, ...])

Isotonic regression-based monotonic binning implementation using clean architecture.

ManualFlexibleBinning(bin_spec[, ...])

Manual flexible binning implementation for user-defined mixed bin types.

SingletonBinning([preserve_dataframe, ...])

Singleton binning implementation using clean architecture.