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))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.
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).
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.
| Name | Description |
|---|---|
PrismBoostClassifier |
Primary classifier (sklearn-compatible) |
PrismBoostRegressor |
Primary regressor |
SEFRBoostClassifier / SEFRBoostRegressor |
Compatibility aliases |
SEFR |
Linear weak learner used inside oblique splits |
- 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)
pip install -e ".[dev]"
pytest -qMIT — see LICENSE.
Third-party note: prismboost._utils includes code derived from wnb under BSD-3-Clause (src/prismboost/licenses/BSD-3-Clause.txt).
If you use PrismBoost in academic work, please cite the accompanying paper (to be updated on publication).
- Hamidreza Keshavarz
- Reza Rawassizadeh