Pythonで学ぶアンサンブル手法
Román de las Heras
Data Scientist, Appodeal
集合知
特性
賢い集団の条件
from sklearn.ensemble import VotingClassifier clf_voting = VotingClassifier( estimators=[ ('label1', clf_1), ('label2', clf_2), ('labelN', clf_N)])
from sklearn.ensemble import VotingClassifier
clf_voting = VotingClassifier( estimators=[ ('label1', clf_1), ('label2', clf_2), ('labelN', clf_N)])
性能を評価
# Get the accuracy score acc = accuracy_score(y_test, y_pred) print("Accuracy: {:0.3f}".format(acc))
Accuracy: 0.938
# Create the individual models clf_knn = KNeighborsClassifier(5) clf_dt = DecisionTreeClassifier() clf_lr = LogisticRegression() # Create voting classifier clf_voting = VotingClassifier( estimators=[ ('knn', clf_knn), ('dt', clf_dt), ('lr', clf_lr)]) # Fit it to the training set and predict clf_voting.fit(X_train, y_train) y_pred = clf_voting.predict(X_test)
# Create the individual models clf_knn = KNeighborsClassifier(5) clf_dt = DecisionTreeClassifier() clf_lr = LogisticRegression()
# Create voting classifier clf_voting = VotingClassifier( estimators=[ ('knn', clf_knn), ('dt', clf_dt), ('lr', clf_lr)])
# Fit it to the training set and predict clf_voting.fit(X_train, y_train) y_pred = clf_voting.predict(X_test)
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