第十五周（scikt-learn） python

题目叙述
文章图片

代码 ??按照PPT上的教程进行代码的编写即可，代码如下：

from sklearn import metrics from sklearn import datasets from sklearn import cross_validation from sklearn.naive_bayes import GaussianNB from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier# Datasets dataset = datasets.make_classification(n_samples=1000, n_features=10)# Cross-validation kf = cross_validation.KFold(1000, n_folds=10, shuffle=True) for train_index, test_index in kf: X_train, y_train = dataset[0][train_index], dataset[1][train_index] X_test, y_test = dataset[0][test_index], dataset[1][test_index]# GaussianNB GaussianNB_clf = GaussianNB() GaussianNB_clf.fit(X_train, y_train) GaussianNB_pred = GaussianNB_clf.predict(X_test)# SVM SVC_clf = SVC(C=1e-01, kernel='rbf', gamma=0.1) SVC_clf.fit(X_train, y_train) SVC_pred = SVC_clf.predict(X_test)# Random Forest Random_Forest_clf = RandomForestClassifier(n_estimators=6) Random_Forest_clf.fit(X_train, y_train) Random_Forest_pred = Random_Forest_clf.predict(X_test)# Evaluate the cross-validated performance # GaussianNB GaussianNB_accuracy_score = metrics.accuracy_score(y_test, GaussianNB_pred) GaussianNB_f1_score = metrics.f1_score(y_test, GaussianNB_pred) GaussianNB_roc_auc_score = metrics.roc_auc_score(y_test, GaussianNB_pred) print("GaussianNB_accuracy_score: ", GaussianNB_accuracy_score) print("GaussianNB_f1_score: ", GaussianNB_f1_score) print("GaussianNB_roc_auc_score: ", GaussianNB_roc_auc_score)# SVC SVC_accuracy_score = metrics.accuracy_score(y_test, SVC_pred) SVC_f1_score = metrics.f1_score(y_test, SVC_pred) SVC_roc_auc_score = metrics.roc_auc_score(y_test, SVC_pred) print("\nSVC_accuracy_score: ", SVC_accuracy_score) print("SVC_f1_score: ", SVC_f1_score) print("SVC_roc_auc_score: ", SVC_roc_auc_score)# Random_Forest Random_Forest_accuracy_score = metrics.accuracy_score(y_test, Random_Forest_pred) Random_Forest_f1_score = metrics.f1_score(y_test, Random_Forest_pred) Random_Forest_roc_auc_score = metrics.roc_auc_score(y_test, Random_Forest_pred) print("\nRandom_Forest_accuracy_score: ", Random_Forest_accuracy_score) print("Random_Forest_f1_score: ", Random_Forest_f1_score) print("Random_Forest_roc_auc_score: ", Random_Forest_roc_auc_score)

结果

【第十五周（scikt-learn）】GaussianNB_accuracy_score: 0.97
GaussianNB_f1_score: 0.9690721649484536
GaussianNB_roc_auc_score: 0.9716981132075472
SVC_accuracy_score: 0.99
SVC_f1_score: 0.9894736842105264
SVC_roc_auc_score: 0.9905660377358491
Random_Forest_accuracy_score: 0.97
Random_Forest_f1_score: 0.968421052631579
Random_Forest_roc_auc_score: 0.9704937775993576