【机器学习】模型融合Ensemble和集成学习Stacking的实现

原理

（1）模型融合 （2）集成学习

实现

参考资料

from mlxtend.classifier import EnsembleVoteClassifier
from mlxtend.classifier import StackingClassifier
from lightgbm import LGBMClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB 
from sklearn.ensemble import RandomForestClassifier

if model_type == 'ensemble':
     clf1 = LogisticRegression(random_state=0)
     clf2 = XGBClassifier(random_state=0)
     clf3 = SVC(random_state=0, kernel='linear', probability=True)
     clf4 = MLPClassifier(random_state=0)
     model = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3, clf4],
                                    weights=[1, 2, 2, 1], voting='soft', verbose=2)
elif model_type == 'stack':

    clf1 = XGBClassifier(random_state=0)
    clf2 = SVC(random_state=0, kernel='linear', probability=True)
    clf3 = MLPClassifier(random_state=0)
    lr = LogisticRegression()
    model = StackingClassifier(classifiers=[clf1, clf2, clf3],
                               use_probas=True,
                               average_probas=False,
                               meta_classifier=lr)