# -*- coding: utf-8 -*- ''' Created on 2018年1月19日 @author: Jason.F @summary: 混淆矩陣：TP、TN、FP、FP、FN F1：召回率和爭取率 ''' import pandas as pd from sklearn.preprocessing import LabelEncoder from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline from sklearn.svm import SVC import numpy as np from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt from sklearn.metrics import precision_score, recall_score, f1_score #導入數據 df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data',header=None) X=df.loc[:,2:].values y=df.loc[:,1].values le=LabelEncoder() y=le.fit_transform(y)#類標整數化 print (le.transform(['M','B'])) #劃分訓練集合測試集 X_train,X_test,y_train,y_test = train_test_split (X,y,test_size=0.20,random_state=1) #建立pipeline pipe_svc=Pipeline([('scl',StandardScaler()),('clf',SVC(random_state=1))]) pipe_svc.fit(X_train,y_train) y_pred=pipe_svc.predict(X_test) #混淆矩陣并可視化 confmat= confusion_matrix(y_true=y_test,y_pred=y_pred)#輸出混淆矩陣 print (confmat) fig,ax = plt.subplots(figsize=(2.5,2.5)) ax.matshow(confmat,cmap=plt.cm.Blues,alpha=0.3) for i in range(confmat.shape[0]):for j in range(confmat.shape[1]):ax.text(x=j,y=i,s=confmat[i,j],va='center',ha='center') plt.xlabel('predicted label') plt.ylabel('true label') plt.show() #召回率、準確率、F1 print ('precision:%.3f' %precision_score(y_true=y_test, y_pred=y_pred)) print ('recall:%.3f' %recall_score(y_true=y_test, y_pred=y_pred)) print ('F1:%.3f' %f1_score(y_true=y_test, y_pred=y_pred))#參考：http://scikit-learn.org/stable/modules/model_evaluation.html

結果：

[1 0] [[71 1][ 2 40]] precision:0.976 recall:0.952 F1:0.964

總結

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