sklearn习题

Step1：

from sklearn import datasets dataset = datasets.make_classification(n_samples=1000, n_features=10, n_informative=2, n_redundant=2, n_repeated=0, n_classes=2) print(dataset)

Step2：

from sklearn.model_selection import KFold kf = KFold(n_splits=10) for train_index, test_index in kf.split(dataset[0]):     X_train, X_test = dataset[0][train_index], dataset[0][test_index]     y_train, y_test = dataset[1][train_index], dataset[1][test_index]

Step3和Step4：

from sklearn.naive_bayes import GaussianNB from sklearn import metrics def test_GaussianNB(X_train, y_train, X_test, y_test):     clf = GaussianNB()     clf.fit(X_train, y_train)     pred = clf.predict(X_test)     print("GaussianNB: ")     acc = metrics.accuracy_score(y_test, pred)     print('acc: ', acc)     f1 = metrics.f1_score(y_test, pred)     print('f1: ', f1)     auc = metrics.roc_auc_score(y_test, pred)     print('auc: ', auc)   from sklearn.svm import SVC def test_SVC(X_train, y_train, X_test, y_test):     clf = SVC(C=1e-01, kernel='rbf', gamma=0.1)     clf.fit(X_train, y_train)     pred = clf.predict(X_test)     print("SVC: ")     acc = metrics.accuracy_score(y_test, pred)     print('acc: ', acc)     f1 = metrics.f1_score(y_test, pred)     print('f1: ', f1)     auc = metrics.roc_auc_score(y_test, pred)     print('auc: ', auc)   from sklearn.ensemble import RandomForestClassifier def test_nRandomForest(X_train, y_train, X_test, y_test):     clf = RandomForestClassifier(n_estimators=6)     clf.fit(X_train, y_train)     pred = clf.predict(X_test)     print("RandomForest: ")     acc = metrics.accuracy_score(y_test, pred)     print('acc: ', acc)     f1 = metrics.f1_score(y_test, pred)     print('f1: ', f1)     auc = metrics.roc_auc_score(y_test, pred)     print('auc: ', auc)

测试：

k = 1 for train_index, test_index in kf.split(dataset[0]):     X_train, X_test = dataset[0][train_index], dataset[0][test_index]     y_train, y_test = dataset[1][train_index], dataset[1][test_index]     print('test ', k, ': ')     test_GaussianNB(X_train, y_train, X_test, y_test)     test_SVC(X_train, y_train, X_test, y_test)     test_nRandomForest(X_train, y_train, X_test, y_test)     print('\n')     k += 1

结果：

test  1 :  GaussianNB:  acc:  0.93 f1:  0.9391304347826087 auc:  0.9242424242424243 SVC:  acc:  0.93 f1:  0.9380530973451328 auc:  0.9262626262626262 RandomForest:  acc:  0.93 f1:  0.9369369369369368 auc:  0.9282828282828283   test  2 :  GaussianNB:  acc:  0.91 f1:  0.9158878504672897 auc:  0.9089635854341738 SVC:  acc:  0.94 f1:  0.9423076923076923 auc:  0.9395758303321329 RandomForest:  acc:  0.95 f1:  0.9514563106796117 auc:  0.9497799119647861   test  3 :  GaussianNB:  acc:  0.97 f1:  0.9696969696969697 auc:  0.9701880752300921 SVC:  acc:  0.91 f1:  0.9052631578947369 auc:  0.9093637454981992 RandomForest:  acc:  0.93 f1:  0.9263157894736843 auc:  0.9293717486994799   test  4 :  GaussianNB:  acc:  0.89 f1:  0.8865979381443299 auc:  0.8914090726615816 SVC:  acc:  0.91 f1:  0.9072164948453608 auc:  0.9114813327980731 RandomForest:  acc:  0.93 f1:  0.9263157894736843 auc:  0.930349257326375   test  5 :  GaussianNB:  acc:  0.91 f1:  0.9203539823008849 auc:  0.9053945249597423 SVC:  acc:  0.97 f1:  0.9724770642201834 auc:  0.9690016103059581 RandomForest:  acc:  0.95 f1:  0.9514563106796117 auc:  0.9537037037037037   test  6 :  GaussianNB:  acc:  0.9 f1:  0.8936170212765957 auc:  0.9009661835748792 SVC:  acc:  0.93 f1:  0.9230769230769231 auc:  0.928743961352657 RandomForest:  acc:  0.95 f1:  0.945054945054945 auc:  0.9488727858293077   test  7 :  GaussianNB:  acc:  0.96 f1:  0.9545454545454546 auc:  0.9594155844155845 SVC:  acc:  0.96 f1:  0.9545454545454546 auc:  0.9594155844155845 RandomForest:  acc:  0.98 f1:  0.9767441860465117 auc:  0.9772727272727273   test  8 :  GaussianNB:  acc:  0.93 f1:  0.9278350515463919 auc:  0.9335748792270531 SVC:  acc:  0.93 f1:  0.9278350515463919 auc:  0.9335748792270531 RandomForest:  acc:  0.96 f1:  0.9574468085106383 auc:  0.9613526570048309   test  9 :  GaussianNB:  acc:  0.94 f1:  0.9400000000000001 auc:  0.9403761504601841 SVC:  acc:  0.95 f1:  0.9494949494949495 auc:  0.9501800720288115 RandomForest:  acc:  0.95 f1:  0.9494949494949495 auc:  0.9501800720288115   test  10 :  GaussianNB:  acc:  0.95 f1:  0.9557522123893805 auc:  0.9480519480519481 SVC:  acc:  0.94 f1:  0.9464285714285714 auc:  0.9391233766233766 RandomForest:  acc:  0.96 f1:  0.9636363636363636 auc:  0.9618506493506493

Step5：

通过观察这十个测试，可以知道RandomForest是三个算法中最好的，而SVC差一些，朴素贝叶斯是最差的。

文章来源: sklearn习题