kernelboost 0.3.1

Gradient boosting with kernel regression base learners

KernelBoost

Gradient boosting with kernel-based local constant estimators

KernelBoost is a gradient boosting algorithm that uses Nadaraya-Watson (local constant) kernel estimators as base learners instead of decision trees. It has:

• Support for regression, classification and quantile regression tasks.
• sklearn style API (fit, predict).
• CPU (via C) and GPU (via CuPy/CUDA) backends.

Installation

# Basic installation
pip install kernelboost

# With GPU support (requires CUDA)
pip install cupy-cuda12x  # for CUDA 12

Dependencies: NumPy only. CuPy optional for GPU acceleration.

Quick Start

from kernelboost import KernelBooster, MulticlassBooster
from kernelboost.objectives import MSEObjective, EntropyObjective

# Regression
booster = KernelBooster(objective=MSEObjective()).fit(X_train, y_train)
predictions = booster.predict(X_test)

# Binary classification
booster = KernelBooster(objective=EntropyObjective()).fit(X_train, y_train)
logits = booster.predict(X_test)
probabilities = booster.predict_proba(X_test)

# Multiclass classification (fits one booster per class)
booster = MulticlassBooster().fit(X_train, y_train)
class_labels = booster.predict(X_test)

Documentation

For full documentation, benchmarks, architecture details, and API reference, see the GitHub repository.

License

MIT License