【机器学习】模型融合Ensemble和集成学习Stacking的实现

我就是我 2022-11-05 11:50 84阅读 0赞

# 原理 #

（1）[模型融合][Link 1]  
（2）[集成学习][Link 1]

# 实现 #

[参考资料][Link 2]

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)

[Link 1]: https://zhuanlan.zhihu.com/p/186965796
[Link 2]: https://www.programcreek.com/python/example/99824/xgboost.XGBClassifier