机器学习之评价分类结果机器学习之评价分类结果

1 分类准确度及衡量指标
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1.scikit-learn中的混淆矩阵，精准率，召回率

fromsklearn.metrics import confusion_matrix confusion_matrix(y_test,y_log_predict)from sklearn.metricsimport precision_score precision_score(y_test,y_log_predict)from sklearn.metrics import recall_score recall_score(y_test,y_log_predict)

2. F1 Score #调和平均值有一个数小，值都小

from sklearn.metricsimport f1_scoref1_score(y_test,y_log_predict)

3 scikit-learn中的precision-recall曲线

from sklearn.metricsimport precision_recall_curveprecisions,recalls,thresholds = precision_recall_curve(y_test,decision_score)plt.plot(thresholds,precisions[:-1]) plt.plot(thresholds,recalls[:-1]) plt.show()

4. 精准率和召回率的平衡

log_reg.decision_function(X_test) log_reg.decision_function(X_test)[:10]# 根据值是否大于0 二分类from sklearn.metrics importprecision_score from sklearn.metrics importrecall_scoreprecision = [] recalls = [] thresholds = np.arange(np.min(decision_score),np.max(decision_score),0.01) for threshold inthresholds: y_predict = np.array(decision_score>threshold,dtype='int') precision.append(precision_score(y_test,y_predict)) recalls.append(recall_score(y_test,y_predict))plt.plot(thresholds,precision) plt.plot(thresholds,recalls) plt.show()

【机器学习之评价分类结果】5.TPR 与 FPR

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6.ROC曲线

from sklearn.metrics import roc_curvefprs,tprs,thresholds= roc_curve(y_test,decision_score) plt.plot(fprs,tprs) plt.show()from sklearn.metrics import roc_auc_score#计算曲线下方的面积roc_auc_score(y_test,decision_score)

ROC曲线下方的面积越大算法相对越好
7. 多分类中的混淆矩阵

import numpyas np import matplotlib.pyplot as plt from sklearn import datasetsdigits = datasets.load_digits() X = digits.data y = digits.target from sklearn.model_selection import train_test_splitX_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.8,random_state=666) from sklearn.linear_model import LogisticRegressionlog_reg = LogisticRegression() log_reg.fit(X_train,y_train) log_reg.score(X_test,y_test) y_predict = log_reg.predict(X_test) from sklearn.metrics import precision_scoreprecision_score(y_test,y_predict,average='micro')# 多分类 fromsklearn.metricsimportconfusion_matrixconfusion_matrix(y_test,y_predict) cfm = confusion_matrix(y_test,y_predict)plt.matshow(cfm,cmap=plt.cm.gray) plt.show()#图1 row_sums = np.sum(cfm,axis=1) err_matrix = cfm/row_sums np.fill_diagonal(err_matrix,0) err_matrix# 每一行犯错误的百分比 plt.matshow(err_matrix,cmap=plt.cm.gray) plt.show()#图2

图1表示混淆矩阵的图