人工智能和机器学习之分类算法：梯度提升机（GBM）与随机森林的比较.docx

from sklearn.ensemble import GradientBoostingClassifier

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

gbm = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=1, random_s

tate=42)

gbm.fit(X_train, y_train)

predictions = gbm.predict(X_test)

from sklearn.ensemble import RandomForestClassifier

rf = RandomForestClassifier(n_estimators=100, max_depth=1, random_state=42)

rf.fit(X_train, y_train)

predictions = rf.predict(X_test)

from sklearn.metrics import accuracy_score

print(f"Random Forest Accuracy: {rf_accuracy}")

import numpy as np

from sklearn.datasets import load_breast_cancer

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

gbm = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1, max_depth=3, random_s

tate=42)

gbm.fit(X_train, y_train)

predictions = gbm.predict(X_test)