oqboost 2.0.0

OQBoost 2.0 — gradient-boosted 2D-oblique decision trees (histogram-binned, C++ backend)

OQBoost 2.0

Gradient-boosted 2D-oblique decision trees — histogram-binned, C++ backend.

OQBoost splits on oblique hyperplanes over feature pairs (a·u + b·v < t) instead of axis-aligned thresholds, capturing diagonal and interaction boundaries that XGBoost/LightGBM approximate with coarse staircases. Version 2.0 is a ground-up redesign: a histogram-binned 2D-oblique core with a deterministic direction fit.

scikit-learn compatible · compiled C++ (pybind11) + OpenMP.

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Install

pip install oqboost

A prebuilt wheel is provided for macOS (arm64, CPython 3.12). On other platforms pip builds from source — needs a C++17 compiler (clang++/g++) and, for parallelism, OpenMP (brew install libomp on macOS).

Quickstart

from oqboost import OQBoostClassifier, OQBoostRegressor

# Binary classification
clf = OQBoostClassifier(
    n_estimators=120, learning_rate=0.06, max_depth=4,
    max_bins=16, subsample=0.8, colsample=0.8, random_state=42,
)
clf.fit(X_train, y_train)
proba = clf.predict_proba(X_test)[:, 1]   # P(class 1)
pred  = clf.predict(X_test)

# Regression (squared error)
reg = OQBoostRegressor(n_estimators=120, learning_rate=0.06)
reg.fit(X_train, y_train)
y_hat = reg.predict(X_test)

Both are drop-in scikit-learn estimators — usable in Pipeline, GridSearchCV, cross_val_score, and clone.

Key hyperparameters

Param	Default	Meaning
n_estimators	120	boosting rounds
learning_rate	0.06	shrinkage
max_depth	4	interaction depth (stacked 2D cuts)
max_bins	16	grid / direction-seed resolution (keep small)
subsample	0.8	rows per tree (key overfit lever)
colsample	0.8	features per node
reg_lambda	1.0	L2 regularization
n_screen	-1	SIS top-m feature screening (-1 = exhaustive)

Why oblique

Axis-aligned boosters need many stacked cuts to approximate a diagonal boundary. On a 2D XOR problem XGBoost reaches only AUC ≈ 0.53 while OQBoost reaches ≈ 0.92; on a Spiral OQBoost draws the smoothest boundary of all four boosters. Across tuned benchmarks it ranks above XGBoost and LightGBM.

Full benchmarks, decision-boundary figures, and design notes: https://github.com/cree1116/oqboost

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MIT License.