prismboost 0.1.1

PrismBoost: gradient boosting with SEFR oblique splits for classification and regression.

PrismBoost

PrismBoost is a gradient-boosting classifier/regressor that uses SEFR oblique splits at internal nodes (hyperplane splits instead of axis-aligned thresholds), with an optional fast C++ backend.

from prismboost import PrismBoostClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=0, stratify=y
)

clf = PrismBoostClassifier(n_estimators=100, max_depth=3, random_state=0)
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))

Why oblique boosting?

Axis-aligned GBDTs approximate curved boundaries with staircases. PrismBoost fits linear (oblique) splits, so decision surfaces on non-linear problems are typically smoother.

Predicted-probability surfaces on moons: PrismBoost (left) vs XGBoost (right).

Decision boundaries on six synthetic 2D datasets (rows) across classifiers (columns). Lower surface roughness S is smoother.

Benchmark highlights (PMLB)

Evaluated on 121 Penn Machine Learning Benchmark classification datasets against strong baselines (CatBoost, LightGBM, LightGBM-linear, XGBoost, SPORF, Random Forest, Logistic Regression). Hyperparameters are tuned with Optuna; scores are repeated stratified CV.

Median scores (higher is better for F1 / ROC-AUC; lower is better for inference latency):

Model	Median macro-F1	Median ROC-AUC	Median inference (ms/row)
PrismBoost	0.903	0.976	0.056
CatBoost	0.892	0.976	0.184
XGBoost	0.875	0.970	0.615
LightGBM-linear	0.875	0.970	0.567
LightGBM	0.870	0.972	0.550
Random Forest	0.862	0.964	5.140
SPORF	0.856	0.970	10.058
Logistic Regression	0.822	0.942	0.053

Average ranks (1 = best; Friedman tests significant for macro-F1 and ROC-AUC):

Model	Macro-F1 rank	ROC-AUC rank
CatBoost	3.33	3.48
PrismBoost	3.88	4.26
LightGBM-linear	3.99	4.26
LightGBM	4.10	4.10
XGBoost	4.31	4.24
Logistic Regression	5.31	5.66
Random Forest	5.48	5.31
SPORF	5.61	4.70

Nemenyi critical-difference diagrams (α = 0.05). Models connected by a bar are not significantly different.

On these data, PrismBoost is competitive with modern GBDTs on accuracy while remaining among the fastest at inference (second only to logistic regression; fastest non-linear model by median latency).

Install

Requires Python 3.10–3.13, a C++17 compiler, and CMake (for the optional native extension).

pip install .
# or editable:
pip install -e ".[dev]"

If the C++ extension fails to build, the package still works via the pure-Python backend.

Public API

Name	Description
PrismBoostClassifier	Primary classifier (sklearn-compatible)
PrismBoostRegressor	Primary regressor
SEFRBoostClassifier / SEFRBoostRegressor	Compatibility aliases
SEFR	Linear weak learner used inside oblique splits

Features

• Oblique tree splits from SEFR (closed-form linear separator)
• Binary and multiclass classification; regression
• Optional C++ backend for faster fit/predict
• sklearn estimator API (fit, predict, predict_proba, pipelines, pickling)

Quick example

See examples/quickstart.py.

Tests

pip install -e ".[dev]"
pytest -q

License

This project is licensed under the MIT License.

Third-party note: prismboost._utils includes code derived from wnb under the BSD 3-Clause License.

Citation

If you use PrismBoost in academic work, please cite the accompanying paper (to be updated on publication).

Authors

• Hamidreza Keshavarz
• Reza Rawassizadeh