actual.py 文件源码

def makEnsemble( X, xlist, Y ):
    #naive bayes
    clf = MultinomialNB()
    clf.fit( xlist, Y )
    featureSelectModel.append (clf)

    #K nearest neighbours
    clf = KNeighborsClassifier()
    clf.fit( xlist, Y )
    featureSelectModel.append (clf)

    #Logistic regression
    clf = LogisticRegression(C=1)
    clf.fit( xlist, Y )
    featureSelectModel.append (clf)

    #random forest
    clf  = RandomForestClassifier(n_estimators = 400)
    clf.fit( X, Y )
    wholeFeatureModel.append (clf)

    #extra forest
    clf = ExtraTreesClassifier(n_estimators = 400)
    clf.fit( X, Y )
    wholeFeatureModel.append (clf)

    #decision forest
    clf = DecisionTreeClassifier(max_depth=None, min_samples_split=1, random_state=0)
    clf.fit( X, Y )
    wholeFeatureModel.append (clf)

    #gradient boosting
    params = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 1,
                  'learning_rate': 0.01}
    clf = GradientBoostingClassifier(**params)
    clf.fit( X, Y )
    wholeFeatureModel.append (clf)