Ансамблеві методи в Python
Román de las Heras
Data Scientist, Appodeal
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Фактичне значення ~ середнє(оцінок)
Властивості
Averaging Classifier
from sklearn.ensemble import VotingClassifier clf_voting = VotingClassifier( estimators=[ ('label1', clf_1), ('label2', clf_2), ... ('labelN', clf_N)], voting='soft', weights=[w_1, w_2, ..., w_N] )
from sklearn.ensemble import VotingClassifier
clf_voting = VotingClassifier( estimators=[ ('label1', clf_1), ('label2', clf_2), ... ('labelN', clf_N)], voting='soft', weights=[w_1, w_2, ..., w_N] )
Averaging Regressor
from sklearn.ensemble import VotingRegressor reg_voting = VotingRegressor( estimators=[ ('label1', reg_1), ('label2', reg_2), ... ('labelN', reg_N)], weights=[w_1, w_2, ..., w_N] )
from sklearn.ensemble import VotingRegressor
reg_voting = VotingRegressor( estimators=[ ('label1', reg_1), ('label2', reg_2), ... ('labelN', reg_N)], weights=[w_1, w_2, ..., w_N] )
# Instantiate the individual models clf_knn = KNeighborsClassifier(5) clf_dt = DecisionTreeClassifier() clf_lr = LogisticRegression()
# Create an averaging classifier clf_voting = VotingClassifier( estimators=[ ('knn', clf_knn), ('dt', clf_dt), ('lr', clf_lr)], voting='soft', weights=[1, 2, 1] )
Ціль:
Ознаки:
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