sklearn习题

高级编程技术sklearn习题

题目：

代码：

#!/usr/bin/env python
# -*- coding: utf-8 -*-  

import sklearn.datasets as sd
import sklearn.metrics as sm
import sklearn.model_selection as sms
import sklearn.naive_bayes as sn
import sklearn.svm as ss
import sklearn.ensemble as se


def test(alg,data_train,data_test,target_train,target_test):
    if(alg=='GaussianNB'):
        t = sn.GaussianNB()
    elif(alg=='SVC'):
        t = ss.SVC(C=1e-1,kernel='rbf')
    elif(alg=='RandomForestClassifier'):
        t = se.RandomForestClassifier(n_estimators=100)
    t.fit(data_train,target_train)
    pre = t.predict(data_test)
    print()
    print(alg + " test result:")
    print("Accuracy: ", sm.accuracy_score(target_test, pre))
    print("F1-score : ", sm.f1_score(target_test, pre))
    print("AUC RO: ", sm.roc_auc_score(target_test, pre))

def main(args):
    dataset = sd.make_classification(n_samples=1000, n_features=10)
    kfold = sms.KFold(n_splits=10)
    testid = 1
    for tr, te in kfold.split(dataset[0]):
        print("testid:" + str(testid))
        testid += 1
        alglist = ['GaussianNB','SVC','RandomForestClassifier']
        for alg in alglist:
            test(
                alg=alg,data_train=dataset[0][tr],
                data_test=dataset[0][te],
                target_train=dataset[1][tr],
                target_test=dataset[1][te]
                )
        print()
    return 0

if __name__ == '__main__':
    import sys
    sys.exit(main(sys.argv))

结果：

总结：三个算法都在训练结果的评价中获得0.9左右的评价，有良好的训练结果。