Sklearn的metrics模組下有多個計算模型評價指標的函式，本文只介紹二分類的指標函式。

1.準確率

1.1引數說明

sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)

1.2 應用

import pandas as pd
import numpy as np
from sklearn import metrics
y_pred = [0,1,0,1]
y_true = [0,0,0,0]

## 返回正確率
metrics.accuracy_score(y_pred,y_true)

0.5

## 返回正確個數
metrics.accuracy_score(y_pred,y_true,normalize=False
 
)

2

2.混淆矩陣

2.1 引數說明

2.2 應用

y_true = [2, 0, 2, 0, 0, 1]
y_pred = [0, 0, 2, 2, 3, 2]
metrics.confusion_matrix(y_true, y_pred)

array([[1, 0, 1, 1],
       [0, 0, 1, 0],
       [1, 0, 1, 0],
       [0, 0, 0, 0]], dtype=int64)
 

import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)

    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.tight_layout()

y_test = [1, 1, 1, 0]
y_pred = [1, 1, 0, 0]
# Compute confusion matrix
# #注意混淆矩陣要求預測結果在第一個位置
cnf_matrix = confusion_matrix(y_test, y_pred)
## 限制兩位小數
np.set_printoptions(precision=2)

# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=[0, 1], title='Confusion matrix, without normalization')
plt.show()

Confusion matrix, without normalization
[[1 0]
 [1 2]]

# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(
    cnf_matrix,
    classes=[0, 1],
    normalize=True,
    title='Normalized confusion matrix')

plt.show()

Normalized confusion matrix
[[ 1.    0.  ]
 [ 0.33  0.67]]



<matplotlib.figure.Figure at 0xc2022b0>

3. Recall&Precision

3.1 引數說明

3.2 應用

  要注意y_true和y_pred的位置順序。

y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ['class 0', 'class 1', 'class 2']
print(metrics.classification_report(y_true, y_pred))

             precision    recall  f1-score   support

          0       0.50      1.00      0.67         1
          1       0.00      0.00      0.00         1
          2       1.00      0.67      0.80         3

avg / total       0.70      0.60      0.61         5

print(metrics.classification_report(y_true, y_pred, target_names=target_names))

             precision    recall  f1-score   support

    class 0       0.50      1.00      0.67         1
    class 1       0.00      0.00      0.00         1
    class 2       1.00      0.67      0.80         3

avg / total       0.70      0.60      0.61         5

4.Roc&Auc

  計算AUC，畫ROC曲線的貌似沒有直接的函式。

4.1 引數

y_true = np.array([0, 0, 1, 1])
y_scores = np.array([0.1, 0.4, 0.35, 0.8])
metrics.roc_auc_score(y_true, y_scores)

0.75

                         2018-07-16 於南京市建鄴區新城科技園