Gradient Boosting for classification. The Gradient Boosting Classifier is an additive ensemble of a base model whose error is corrected in successive iterations (or stages) by the addition of Regression Trees which correct the residuals (the error of the previous stage).
from sklearn.ensemble import GradientBoostingClassifier
Create some toy classification data
from sklearn.datasets import load_iris iris_dataset = load_iris() X, y = iris_dataset.data, iris_dataset.target
Let us split this data into training and testing set.
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=0)
GradientBoostingClassifier model using the default params.
gbc = GradientBoostingClassifier() gbc.fit(X_train, y_train)
Let us score it on the test set
# We are using the default classification accuracy score >>> gbc.score(X_test, y_test) 1
By default there are 100 estimators built
>>> gbc.n_estimators 100
This can be controlled by setting
n_estimators to a different value during the initialization time.