創建樸素貝葉斯分類器、交叉驗證

樸素貝葉斯分類器

其知識詳見：http://blog.csdn.net/batuwuhanpei/article/details/51910349

• 導入模塊

import numpy as np
from sklearn.naive_bayes import GaussianNB
from func_plot_classifier import plot_classifier

• 加載數據

input_file= 'data_multivar.txt'
x = []
y = []
with open(input_file,'r') as f:
    for line in f.readlines():
        data=[float(i) for i in line.split(',')]
        x.append(data[:-1])
        y.append(data[-1])
x=np.array(x)
y=np.array(y)

• 將數據分割成訓練集和測試集

from sklearn import cross_validation
x_train,x_test,y_train,y_test= cross_validation.train_test_split(x,y,test_size=0.25,random_state=5)

• 建立一個樸素貝葉斯分類器

gaussiannb_classifier = GaussianNB()
gaussiannb_classifier.fit(x_train,y_train)

• 計算分類器的準確性

y_predict = gaussiannb_classifier.predict(x_test)
accuracy= 100*(y_predict==y_test).sum()/x_test.shape[0]
print("Accuracy of the GuassianNb Classifier:  ",round(accuracy,2),'%')

代碼運行結果：
Accuracy of the GuassianNb Classifier: 98.0 %

• 繪圖

plot_classifier(gaussiannb_classifier,x_test,y_test)

• 交叉驗證

  • 知識前備

有100個樣品，其中有83個及格。分類器分出73個認爲及格的，但其中只有65個及格的。

精度（precision）= 65/73

召回率（recall）= 65/83

F1得分（F1 Scole）= 2*精度*召回率/(精度+召回率)

精度和召回率是二律背反的，不能同時具備。

num_validation=5
accuracy = cross_validation.cross_val_score(gaussiannb_classifier,x,y,scoring='accuracy',cv=num_validation)
print("Accuracy = ",round(100*accuracy.mean(),5),'%')

precision = cross_validation.cross_val_score(gaussiannb_classifier,x,y,scoring='precision_weighted',cv=num_validation)
print("Accuracy = ",round(100*precision.mean(),5),'%')

recall = cross_validation.cross_val_score(gaussiannb_classifier,x,y,scoring='recall_weighted',cv=num_validation)
print("Accuracy = ",round(100*recall.mean(),5),'%')

F1 = cross_validation.cross_val_score(gaussiannb_classifier,x,y,scoring='f1_weighted',cv=num_validation)
print("Accuracy = ",round(100*F1.mean(),5),'%')

代碼運行結果：
Accuracy = 99.5 %
Accuracy = 99.52381 %
Accuracy = 99.5 %
Accuracy = 99.49969 %

• 直接提取性能報告

from sklearn.metrics import classification_report
names=['Class 0','Class 1','Class 2','Class 3']
print(classification_report(y_test,y_predict,target_names=names))

代碼運行結果：

             precision    recall  f1-score   support

    Class 0       0.96      1.00      0.98        23
    Class 1       1.00      0.95      0.98        21
    Class 2       0.97      1.00      0.99        34
    Class 3       1.00      0.95      0.98        22

avg / total       0.98      0.98      0.98       100