calculate precision and recall in a confusion matrix

Question

Suppose I have a confusion matrix as like as below. How can I calculate precision and recall?

Answer 1

Given:

hypothetical confusion matrix (cm)

cm = 
[[ 970    1    2    1    1    6   10    0    5    0]
 [   0 1105    7    3    1    6    0    3   16    0]
 [   9   14  924   19   18    3   13   12   24    4]
 [   3   10   35  875    2   34    2   14   19   19]
 [   0    3    6    0  903    0    9    5    4   32]
 [   9    6    4   28   10  751   17    5   24    9]
 [   7    2    6    0    9   13  944    1    7    0]
 [   3   11   17    3   16    3    0  975    2   34]
 [   5   38   10   16    7   28    5    4  830   20]
 [   5    3    5   13   39   10    2   34    5  853]]

Goal:

precision and recall for each class using map() to calculate list division.

from operator import truediv
import numpy as np

tp = np.diag(cm)
prec = list(map(truediv, tp, np.sum(cm, axis=0)))
rec = list(map(truediv, tp, np.sum(cm, axis=1)))
print ('Precision: {}\nRecall: {}'.format(prec, rec))

Result:

Precision: [0.959, 0.926, 0.909, 0.913, 0.896, 0.880, 0.941, 0.925, 0.886, 0.877]
Recall:    [0.972, 0.968, 0.888, 0.863, 0.937, 0.870, 0.954, 0.916, 0.861, 0.880]

please note: 10 classes, 10 precisions and 10 recalls.