PaloBst is an over-fitting robust Gradient Boosting Decision Tree algorithm. The details of the algorithm are illustrated in "Tackling Overfitting in Boosting for Noisy Healthcare Data, IEEE TKDE".
To use the package, you need to install numba >= 0.46.
To install the package, clone the repository, and run:
# cd palobst
$ python setup.py develop
from sklearn.datasets import make_friedman1
from sklearn.model_selection import train_test_split
X, y = make_friedman1(100)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
from palobst import PaloBst
model = PaloBst(distribution="gaussian")
model.warmup() # this runs JIT
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
# feature importances
print(model.feature_importances_)Please see tests/test_regression.py for more details.
from sklearn.datasets import make_hastie_10_2
from sklearn.model_selection import train_test_split
X, y = make_hastie_10_2(100)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
from palobst import PaloBst
model = PaloBst(distribution="bernoulli")
model.warmup() # this runs JIT
model.fit(X_train, y_train)
y_pred = model.predict_proba(X)[:,1]
# feature importances
print(model.feature_importances_)Please see tests/test_classification.py for more details.
- Yubin Park and Joyce C. Ho. Tackling Overfitting in Boosting for Noisy Healthcare Data. IEEE Transactions on Knowledge and Data Engineering. https://ieeexplore.ieee.org/document/8933485
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