Different Classification Metrics with Code Example

Confusino Matrix with a given simple example

	dog	cat
dog	5	2
cat	3	3

	dog	cat
dog	TP	FP
cat	FN	TN

• accuracy = (TP+TN)/(TP+TN+FP+FN)

  >>> from sklearn.metrics import accuracy_score
  >>> y_pred = [0, 2, 1, 3]
  >>> y_true = [0, 1, 2, 3]
  >>> accuracy_score(y_true, y_pred)
  0.5
  

• precision = TP/(TP+FP)

  >>> from sklearn.metrics import precision_score
  >>> y_true = [0, 1, 2, 0, 1, 2]
  >>> y_pred = [0, 2, 1, 0, 0, 1]
  >>> precision_score(y_true, y_pred, average='macro')
  0.22
  
  

• recall = TP/(TP+FN)

  >>> from sklearn.metrics import recall_score
  >>> y_true = [0, 1, 2, 0, 1, 2]
  >>> y_pred = [0, 2, 1, 0, 0, 1]
  >>> recall_score(y_true, y_pred, average='macro')
  0.33...
  
  

• f1 = 2TP/(2TP+FP+FN)

  >>> from sklearn.metrics import f1_score
  >>> y_true = [0, 1, 2, 0, 1, 2]
  >>> y_pred = [0, 2, 1, 0, 0, 1]
  >>> f1_score(y_true, y_pred, average='macro')
  0.26...
  >>> f1_score(y_true, y_pred, average='micro')
  0.33...
  >>> f1_score(y_true, y_pred, average='weighted')
  0.26...
  >>> f1_score(y_true, y_pred, average=None)
  array([0.8, 0. , 0. ])
  
  

• mcc = (TPxTN-FPxFN)/(sqrt(TP+FP)(TP+FN)(TN+FP)(TN+FN))

  >>> from sklearn.metrics import matthews_corrcoef
  >>> y_true = [+1, +1, +1, -1]
  >>> y_pred = [+1, -1, +1, +1]
  >>> matthews_corrcoef(y_true, y_pred)
  -0.33...
  

• confusion matrix

  >>> from sklearn.metrics import confusion_matrix
  >>> y_true = [2, 0, 2, 2, 0, 1]
  >>> y_pred = [0, 0, 2, 2, 0, 2]
  >>> confusion_matrix(y_true, y_pred)
  array([[2, 0, 0],
         [0, 0, 1],
         [1, 0, 2]])