Confusion Matrix In Multiclass Classification
May 24, 2019
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, random_state=666, test_size=0.8)
from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
log_reg.score(X_test, y_test)
0.93115438108484
y_predict = log_reg.predict(X_test)
from sklearn.metrics import precision_score
precision_score(y_test, y_predict, average="micro")
0.93115438108484
from sklearn.metrics import confusion_matrix
cfm = confusion_matrix(y_test, y_predict)
cfm
array([[147, 0, 1, 0, 0, 1, 0, 0, 0, 0],
[ 0, 123, 1, 2, 0, 0, 0, 3, 4, 10],
[ 0, 0, 134, 1, 0, 0, 0, 0, 1, 0],
[ 0, 0, 0, 138, 0, 5, 0, 1, 5, 0],
[ 2, 5, 0, 0, 139, 0, 0, 3, 0, 1],
[ 1, 3, 1, 0, 0, 146, 0, 0, 1, 0],
[ 0, 2, 0, 0, 0, 1, 131, 0, 2, 0],
[ 0, 0, 0, 1, 0, 0, 0, 132, 1, 2],
[ 1, 9, 2, 3, 2, 4, 0, 0, 115, 4],
[ 0, 1, 0, 5, 0, 3, 0, 2, 2, 134]], dtype=int64)
plt.matshow(cfm, cmap=plt.cm.gray)
plt.show()
对矩阵做处理，突出错误
# 每行求和
row_sums = np.sum(cfm, axis=1)
# 求出每个格子占本行比例
err_matrix = cfm / row_sums
# 对角线置0
np.fill_diagonal(err_matrix, 0)
err_matrix
array([[0. , 0. , 0.00735294, 0. , 0. ,
0.00657895, 0. , 0. , 0. , 0. ],
[0. , 0. , 0.00735294, 0.01342282, 0. ,
0. , 0. , 0.02205882, 0.02857143, 0.06802721],
[0. , 0. , 0. , 0.00671141, 0. ,
0. , 0. , 0. , 0.00714286, 0. ],
[0. , 0. , 0. , 0. , 0. ,
0.03289474, 0. , 0.00735294, 0.03571429, 0. ],
[0.01342282, 0.03496503, 0. , 0. , 0. ,
0. , 0. , 0.02205882, 0. , 0.00680272],
[0.00671141, 0.02097902, 0.00735294, 0. , 0. ,
0. , 0. , 0. , 0.00714286, 0. ],
[0. , 0.01398601, 0. , 0. , 0. ,
0.00657895, 0. , 0. , 0.01428571, 0. ],
[0. , 0. , 0. , 0.00671141, 0. ,
0. , 0. , 0. , 0.00714286, 0.01360544],
[0.00671141, 0.06293706, 0.01470588, 0.02013423, 0.01333333,
0.02631579, 0. , 0. , 0. , 0.02721088],
[0. , 0.00699301, 0. , 0.03355705, 0. ,
0.01973684, 0. , 0.01470588, 0.01428571, 0. ]])
plt.matshow(err_matrix, cmap=plt.cm.gray)
plt.show()
可以看出许多8被预测为1，很多1被预测为9，我们可以相应的做优化，可以分解为两个二分类问题，通过调节1和8,1和9之间的阈值来优化
有些时候也可能是样本的问题，我们不能通过算法解决这个问题，这时候我们要回到数据中