本文主要是介绍sklearn工具包---分类效果评估（acc、recall、F1、ROC、回归、距离），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
一、acc、recall、F1、混淆矩阵、分类综合报告
1、准确率
第一种方式：accuracy_score
# 准确率
import numpy as np
from sklearn.metrics import accuracy_score
y_pred = [0, 2, 1, 3,9,9,8,5,8]
y_true = [0, 1, 2, 3,2,6,3,5,9] #共9个数据，3个相同accuracy_score(y_true, y_pred)
Out[127]: 0.33333333333333331accuracy_score(y_true, y_pred, normalize=False) # 类似海明距离，每个类别求准确后，再求微平均
Out[128]: 3宏平均比微平均更合理，但也不是说微平均一无是处，具体使用哪种评测机制，还是要取决于数据集中样本分布。
宏平均（Macro-averaging），是先对每一个类统计指标值，然后在对所有类求算术平均值。
微平均（Micro-averaging），是对数据集中的每一个实例不分类别进行统计建立全局混淆矩阵，然后计算相应指标。（来源：谈谈评价指标中的宏平均和微平均）
from sklearn import metrics
metrics.precision_score(y_true, y_pred, average='micro') # 微平均，精确率
Out[130]: 0.33333333333333331metrics.precision_score(y_true, y_pred, average='macro') # 宏平均，精确率
Out[131]: 0.375metrics.precision_score(y_true, y_pred, labels=[0, 1, 2, 3], average='macro') # 指定特定分类标签的精确率
Out[133]: 0.5其中average参数有五种：(None, ‘micro’, ‘macro’, ‘weighted’, ‘samples’)
2、召回率
metrics.recall_score(y_true, y_pred, average='micro')
Out[134]: 0.33333333333333331metrics.recall_score(y_true, y_pred, average='macro')
Out[135]: 0.31253、F1
metrics.f1_score(y_true, y_pred, average='weighted')
Out[136]: 0.37037037037037035
4、混淆矩阵
# 混淆矩阵
from sklearn.metrics import confusion_matrix
confusion_matrix(y_true, y_pred)Out[137]:
array([[1, 0, 0, ..., 0, 0, 0],[0, 0, 1, ..., 0, 0, 0],[0, 1, 0, ..., 0, 0, 1],..., [0, 0, 0, ..., 0, 0, 1],[0, 0, 0, ..., 0, 0, 0],[0, 0, 0, ..., 0, 1, 0]])横为true label 竖为predict
5、 分类报告
# 分类报告：precision/recall/fi-score/均值/分类个数from sklearn.metrics import classification_reporty_true = [0, 1, 2, 2, 0]y_pred = [0, 0, 2, 2, 0]target_names = ['class 0', 'class 1', 'class 2']print(classification_report(y_true, y_pred, target_names=target_names))
其中的结果：
precision recall f1-score supportclass 0 0.67 1.00 0.80 2class 1 0.00 0.00 0.00 1class 2 1.00 1.00 1.00 2avg / total 0.67 0.80 0.72 5包含：precision/recall/fi-score/均值/分类个数
6、 kappa score
kappa score是一个介于(-1, 1)之间的数. score>0.8意味着好的分类；0或更低意味着不好（实际是随机标签）
from sklearn.metrics import cohen_kappa_scorey_true = [2, 0, 2, 2, 0, 1]y_pred = [0, 0, 2, 2, 0, 2]cohen_kappa_score(y_true, y_pred)二、ROC
1、计算ROC值
import numpy as npfrom sklearn.metrics import roc_auc_scorey_true = np.array([0, 0, 1, 1])y_scores = np.array([0.1, 0.4, 0.35, 0.8])roc_auc_score(y_true, y_scores)2、ROC曲线
y = np.array([1, 1, 2, 2])scores = np.array([0.1, 0.4, 0.35, 0.8])fpr, tpr, thresholds = roc_curve(y, scores, pos_label=2)来看一个官网例子，贴部分代码，全部的code见：Receiver Operating Characteristic (ROC)
import numpy as np
import matplotlib.pyplot as plt
from itertools import cyclefrom sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp# Import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target# 画图
all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):mean_tpr += interp(all_fpr, fpr[i], tpr[i])# Finally average it and compute AUC
mean_tpr /= n_classesfpr["macro"] = all_fpr
tpr["macro"] = mean_tpr
roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])# Plot all ROC curves
plt.figure()
plt.plot(fpr["micro"], tpr["micro"],label='micro-average ROC curve (area = {0:0.2f})'''.format(roc_auc["micro"]),color='deeppink', linestyle=':', linewidth=4)plt.plot(fpr["macro"], tpr["macro"],label='macro-average ROC curve (area = {0:0.2f})'''.format(roc_auc["macro"]),color='navy', linestyle=':', linewidth=4)colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(n_classes), colors):plt.plot(fpr[i], tpr[i], color=color, lw=lw,label='ROC curve of class {0} (area = {1:0.2f})'''.format(i, roc_auc[i]))plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.show()
三、距离
1、海明距离
from sklearn.metrics import hamming_lossy_pred = [1, 2, 3, 4]y_true = [2, 2, 3, 4]hamming_loss(y_true, y_pred)
0.252、Jaccard距离
import numpy as npfrom sklearn.metrics import jaccard_similarity_scorey_pred = [0, 2, 1, 3,4]y_true = [0, 1, 2, 3,4]jaccard_similarity_score(y_true, y_pred)
0.5jaccard_similarity_score(y_true, y_pred, normalize=False)
2四、回归
1、 可释方差值（Explained variance score）
from sklearn.metrics import explained_variance_scorey_true = [3, -0.5, 2, 7]y_pred = [2.5, 0.0, 2, 8]explained_variance_score(y_true, y_pred) 2、 平均绝对误差（Mean absolute error）
from sklearn.metrics import mean_absolute_errory_true = [3, -0.5, 2, 7]y_pred = [2.5, 0.0, 2, 8]mean_absolute_error(y_true, y_pred)3、 均方误差（Mean squared error）
from sklearn.metrics import mean_squared_errory_true = [3, -0.5, 2, 7]y_pred = [2.5, 0.0, 2, 8]mean_squared_error(y_true, y_pred)4、中值绝对误差（Median absolute error）
from sklearn.metrics import median_absolute_errory_true = [3, -0.5, 2, 7]y_pred = [2.5, 0.0, 2, 8]median_absolute_error(y_true, y_pred) from sklearn.metrics import r2_scorey_true = [3, -0.5, 2, 7]y_pred = [2.5, 0.0, 2, 8]r2_score(y_true, y_pred) 参考文献：
sklearn中的模型评估
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