目录

• 一.混淆矩阵
• 二.精准率和召回率
• 三.Precision-Recall的平衡
• 四.ROC曲线
• 五.多分类问题中的混淆矩阵

一.混淆矩阵

二.精准率和召回率

上图说明只看准确率是远远不够的。

测试数据：

from sklearn import datasets
from sklearn.model_selection import train_test_split

digits = datasets.load_digits()
X = digits.data
y = digits.target.copy()
 
y[digits.target==9] = 1
y[digits.target!=9] = 0

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)

逻辑回归预测：

from sklearn.linear_model import LogisticRegression
 
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
log_reg.score(X_test, y_test)           #0.9755555555555555
y_predict = log_reg.predict(X_test)

三.Precision-Recall的平衡

左边为0，右边为1，五角星为1，圆圈为0

精准率增大时召回率降低

精准率高，则对特别有把握时才预测对，则以前本该算预测对的，变成不对，召回率就越低

召回率高，降低判断， 10%的概率也说有病

自定义曲线：

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
 
digits = datasets.load_digits()
X = digits.data
y = digits.target.copy()
 
y[digits.target==9] = 1
y[digits.target!=9] = 0
 
from sklearn.model_selection import train_test_split
 
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)
 
from sklearn.linear_model import LogisticRegression
 
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
decision_scores = log_reg.decision_function(X_test)
 
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
 
precisions = []
recalls = []
thresholds = np.arange(np.min(decision_scores), np.max(decision_scores), 0.1)
for threshold in thresholds:
    y_predict = np.array(decision_scores >= threshold, dtype='int')
    precisions.append(precision_score(y_test, y_predict))
    recalls.append(recall_score(y_test, y_predict))

使用sklearn中的包：

from sklearn.metrics import precision_recall_curve
precisions,recalls,thresholds = precision_recall_curve(y_test,decision_scores)

横轴是P，纵轴是R

PR曲线靠外或xy轴的面积大则对应的模型好

四.ROC曲线

五.多分类问题中的混淆矩阵

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
 
digits = datasets.load_digits()
X = digits.data
y = digits.target
 
from sklearn.model_selection import train_test_split
 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.8, random_state=666)
 
from sklearn.linear_model import LogisticRegression
 
log_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_score
 
precision_score(y_test, y_predict,average='micro')