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Glossary term
An ensemble method that builds models one after another, each correcting the errors of the last.
Gradient boosting builds models one after another. Each new small tree is trained to correct the mistakes of the combined models so far, and its output is added with a small weight.
Libraries such as XGBoost and LightGBM are famously strong on tabular data.
Gradient boosting
from sklearn.ensemble import GradientBoostingClassifier
model = GradientBoostingClassifier(n_estimators=200, learning_rate=0.1).fit(X_train, y_train)It wins many data competitions and is a go-to method for structured business data.