Python机器学习：评价分类结果003实现混淆矩阵，精准率和召回率

#实现混淆矩阵，精准率和召回率
import numpy as np
from sklearn import datasets
           

digits = datasets.load_digits()
X = digits.data
y = digits.target.copy()
           

使数据偏斜

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)
log_reg.score(X_test,y_test)
y_log_predict = log_reg.predict(X_test)
           

TN

def TN(y_true,y_predict):
    assert len(y_true) == len(y_test)
    return np.sum((y_true == 0) & (y_predict == 0))
TN(y_test,y_log_predict)
           

FN

def FN(y_true,y_predict):
    assert len(y_true) == len(y_test)
    return np.sum((y_true == 1) & (y_predict == 0))
FN(y_test,y_log_predict)
           

FP

def FP(y_true,y_predict):
    assert len(y_true) == len(y_test)
    return np.sum((y_true == 0) & (y_predict == 1))
FP(y_test,y_log_predict)
           

TP

def TP(y_true,y_predict):
    assert len(y_true) == len(y_test)
    return np.sum((y_true == 1) & (y_predict == 1))
TP(y_test,y_log_predict)
           

混淆矩阵

def confusion_matrix(y_true,y_predict):
    return np.array([
        [TN(y_true,y_predict),FP(y_true,y_predict)],
        [FN(y_true,y_predict),TP(y_true,y_predict)]
    ])
confusion_matrix(y_test,y_log_predict)
           

array([[403,   2],
       [  9,  36]])
           

精准率

def precision_score(y_true,y_predict):
    tp = TP(y_true,y_predict)
    fp = FP(y_true,y_predict)
    try:
        return tp / (tp + fp)
    except:
        return 0.0
precision_score(y_test,y_log_predict)
           

召回率

def recall_score(y_true,y_predict):
    tp = TP(y_true,y_predict)
    fn = FN(y_true,y_predict)
    try:
        return tp / (tp + fn)
    except:
        return 0.0
recall_score(y_test,y_log_predict)
           

scikit-learn中的混淆矩阵，精准率和召回率

#scikit-learn中的混淆矩阵，精准率和召回率
from sklearn.metrics import confusion_matrix
confusion_matrix(y_test,y_log_predict)
           

array([[403,   2],
       [  9,  36]], dtype=int64)
           

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