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1.AdaBoost(Adaptive Boosting)
2.GBM(Gradient Boosting Machine)
3.XGBoost
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Python ´úÂ룺
from
sklearn.ensemble import AdaBoostClassifier #
For Classification
from sklearn.ensemble import AdaBoostRegressor
# For Regression
from skleran.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
clf = AdaBoostClassifier(n_estimators=100, base_estimator=dt,
learning_rate=1)
# Above I have used decision tree as a base
estimator, you can use any ML learner as base
estimator if it accepts sample weight
clf.fit(x_train, y_train)
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from
sklearn.ensemble import GradientBoostingClassifier
# For Classification
from sklearn.ensemble import GradientBoostingRegressor
# For Regression
clf = GradientBoostingClassfier(n_estimators=100,
learning_rate=1.0, max_depth=1)
clf.fit(X_train, y_train)
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