SnapBoost Documentation

SnapBoost is an instance of a Heterogeneous Newton Boosting Machine (HNBM) — a generalized gradient boosting framework that supports the use of various types of learners aside from trees. Snapboost is an HNBM that mixes decision trees and kernel ridge regressors instead of trees alone. SnapBoost is scikit-learn compatible and built on HNBM.

At each boosting round, SnapBoost stochastically selects either a decision tree or an RFF ridge regressor. That mix captures both local, axis-aligned structure and smooth global patterns.

Key properties:

  • Scikit-learn API (fit / predict / score)

  • Classification and regression estimators

  • Heterogeneous base learners (trees + RFF ridge)

  • Built on the HNBM framework

Inspired by SnapBoost: A Heterogeneous Boosting Machine (Parnell et al., NeurIPS 2020).

pip install snapboost

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from snapboost import SnapBoostClassifier

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

model = SnapBoostClassifier(num_iterations=100, learning_rate=0.1, random_state=42)
model.fit(X_train, y_train)
print("Accuracy:", model.score(X_test, y_test))

Contents