第十五周，sklearn

1. Create a classification dataset (n samples ! 1000, n features ! 10)
2. Split the dataset using 10-fold cross validation
3. Train the algorithms
   GaussianNB
   SVC (possible C values [1e-02, 1e-01, 1e00, 1e01, 1e02], RBF kernel)
   RandomForestClassifier (possible n estimators values [10, 100, 1000])
4. Evaluate the cross-validated performance
   Accuracy
   F1-score
   AUC ROC
5. Write a short report summarizing the methodology and the results

只要按照ppt上的教程写代码即可，通过datasets.make_classification生成数据集，通过cross_validation.KFold将数据集划分为训练集和测试集，通过metrics.accuracy_score、metrics.f1_score、metrics.roc_auc_score获得结果。

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("\n  SVC_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("\n  Random_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)

文章来源: 第十五周，sklearn