Examples

Interactive Jupyter notebooks in static/ walk through classification, regression, and hyperparameter exploration. Each notebook trains SnapBoost and compares it against XGBoost and LightGBM on the same splits.

Notebook	Dataset	SnapBoost	XGBoost	LightGBM
Classification.ipynb	Breast Cancer Wisconsin	97.2% accuracy	95.8%	96.5%
Regression.ipynb	Diabetes	R² 0.44, RMSE 55.7	R² 0.38, RMSE 58.4	R² 0.40, RMSE 57.7
Parameter_Exploration.ipynb	Synthetic (piecewise + smooth)	R² 0.986, RMSE 0.170	R² 0.986, RMSE 0.174	R² 0.987, RMSE 0.167

Run the notebooks locally:

pip install -r requirements.txt xgboost lightgbm
jupyter notebook static/

Classification

On the Breast Cancer dataset (250 boosting rounds), SnapBoost achieves the highest test accuracy and fewest misclassifications among the three boosters.

Test accuracy and error count vs XGBoost and LightGBM

Confusion matrix for SnapBoost on the held-out test set:

SnapBoost classification confusion matrix

Regression

On the Diabetes dataset (100 boosting rounds), SnapBoost improves R² and RMSE over tree-only baselines.

R², RMSE, and MAE comparison on Diabetes dataset

Predicted vs. actual disease progression:

SnapBoost fitted curve along BMI (other features held at training medians):

Parameter exploration

On a synthetic dataset mixing piecewise-linear and sinusoidal structure, a mixed ensemble (p_tree=0.8) outperforms trees-only (p_tree=1.0, RMSE 0.174) and ridge-only (p_tree=0.0, RMSE 0.366):

Learned functions along one axis for different p_tree values