Where to use gradient boosting
Catboost
Tree algorithms
Gradient boosting involves three elements:
primarily on heterogeneous data
for both classification and regression
that means it is usualy your first choice
pip install catboost shap ipywidgets sklearn
jupyter nbextension enable --py widgetsnbextension
from catboost import CatBoostClassifier, Pool
train_data = Pool(data=[[1, 4, 5, 6],
[4, 5, 6, 7],
[30, 40, 50, 60]],
label=[1, 1, -1],
weight=[0.1, 0.2, 0.3])
model = CatBoostClassifier(iterations=10)
model.fit(train_data)
preds_class = model.predict(train_data)