python第20次作业（15周）-sklearn

题目：

该题目需要生成一个数据集，用三种不同的机器学习算法进行训练，由此来评估这三种算法的性能（根据给出的标准）。根据提示的步骤来一步步实现。

1.人工生成数据集

根据给出的参数提示，生成数据集。
参考：
http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html

# 1.Create a classification dataset (n_samples >= 1000, n_features >= 10)
dataset = datasets.make_classification(n_samples=1000, n_features=10)

PS：load数据集：
http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html

# load a default dataset
from sklearn import datasets
iris = datasets.load_iris()

2.利用10—fold cross validation划分数据集

参考：
http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

# 2.Split the dataset using 10-fold 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]

3.利用三种方法训练

a.GaussianNB
PS：Naive Bayes：

参考：
http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html

# 3.Train the algorithms and evaluate the cross-validated performance
# GaussianNB
GaussianNB_clf = GaussianNB() 
GaussianNB_clf.fit(X_train, y_train)
GaussianNB_pred = GaussianNB_clf.predict(X_test)

SVM:

参考：
http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

# 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 Classifier：
PS：Random Forest:

参考：
http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html

# 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)

4.性能评估：

Accuracy, F1-score, AUC ROC:

参考：
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html

# 4.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:")
print("  Accuracy: ", GaussianNB_accuracy_score)
print("  F1_score: ", GaussianNB_f1_score)
print("  AUC ROC: ", 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:")
print("  Accuracy: ", SVC_accuracy_score)
print("  F1_score: ", SVC_f1_score)
print("  AUC ROC: ", 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("\nRandomForestClassifier:")
print("  Accuracy: ", Random_Forest_accuracy_score)
print("  F1_score: ", Random_Forest_f1_score)
print("  AUC ROC: ", Random_Forest_roc_auc_score)

结果：

通过比较可以看出第三种算法Random Forest Classifier的性能更优。

Documentation and Reference

Documentation：
http://scikit-learn.org/stable/documentation.html
Reference Manual with class descriptions：
http://scikit-learn.org/stable/modules/classes.html