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 from sklearn import metrics # Datasets dataset = datasets.make_classification(n_samples=1000, n_features=10) # Inspect the data structures print("dataset.data: \n", dataset[0]) print("dataset.target: \n", dataset[1]) # 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] # Inspect the data structures print("X_train: \n", X_train) print("y_train: \n", y_train) print("X_test: \n", X_test) print("y_test: \n", y_test) # Navie Bayes GaussianNB_clf = GaussianNB() GaussianNB_clf.fit(X_train, y_train) GaussianNB_pred = GaussianNB_clf.predict(X_test) # Inspect the data structures print("GaussianNB_pred: \n", GaussianNB_pred) print("y_test: \n", y_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) # Inspect the data structures print("SVC_pred: \n", SVC_pred) print("y_test: \n", y_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) # Inspect the data structures print("Random_Forest_pred: \n", Random_Forest_pred) print("y_test: \n", y_test) # Performance evaluation print() GaussianNB_acc = metrics.accuracy_score(y_test, GaussianNB_pred) print(" GaussianNB_acc: ", GaussianNB_acc) GaussianNB_f1 = metrics.f1_score(y_test, GaussianNB_pred) print(" GaussianNB_f1: ", GaussianNB_f1) GaussianNB_auc = metrics.roc_auc_score(y_test, GaussianNB_pred) print(" GaussianNB_auc: ", GaussianNB_auc) print() SVC_acc = metrics.accuracy_score(y_test, SVC_pred) print(" SVC_acc: ", SVC_acc) SVC_f1 = metrics.f1_score(y_test, SVC_pred) print(" SVC_f1: ", SVC_f1) SVC_auc = metrics.roc_auc_score(y_test, SVC_pred) print(" SVC_auc: ",SVC_auc) print() Random_Forest_acc = metrics.accuracy_score(y_test, Random_Forest_pred) print(" Random_Forest_acc: ", Random_Forest_acc) Random_Forest_f1 = metrics.f1_score(y_test, Random_Forest_pred) print(" Random_Forest_f1: ", Random_Forest_f1) Random_Forest_auc = metrics.roc_auc_score(y_test, Random_Forest_pred) print(" Random_Forest_auc: ", Random_Forest_auc)运行结果：
步骤如下：
2、 将数据集划分为训练集和测试集；
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]
3、设定训练方式；
GaussianNB_clf = GaussianNB()
4、用训练集训练；
GaussianNB_clf.fit(X_train, y_train)
5、生成预测集；
GaussianNB_pred = GaussianNB_clf.predict(X_test)
6、比较预测集和测试集，给出正确性评估，有三种评估方式。
GaussianNB_acc = metrics.accuracy_score(y_test, GaussianNB_pred) print(" GaussianNB_acc: ", GaussianNB_acc) GaussianNB_f1 = metrics.f1_score(y_test, GaussianNB_pred) print(" GaussianNB_f1: ", GaussianNB_f1) GaussianNB_auc = metrics.roc_auc_score(y_test, GaussianNB_pred) print(" GaussianNB_auc: ", GaussianNB_auc)
得分评估：
跑了几次程序，结果都不太一样，三种算法孰优孰劣很难讲，既然他们能被作为库函数，肯定有自己的独特优点。
文章来源: 高级编程技术 sklearn课后习题