Algorithm 1. Pseudocode of Fisher score method.
1.	Initialization
2.	scores = [], index = {}, shapes = {};
3.	Input:
4.	Training_Set;
5.	Begin
6.	For c in num_classes:
7.	index[c] = df_fisher[‘class’] == c;
8.	shapes[c] = df_fisher[index[c]].shape [0];
9.	End for c
10.	For col in df_fisher.columns:
11.	If col == ‘class’:
12.	continue
13.	num = 0; den = 0;
14.	m = df_fisher[col].mean();
15.	For c in num_classes:
16.	num = num+ (shapes[c]/df_fisher.shape [0]) $*$   (m-df_fisher[index[c]][col].mean()).^2;
17.	den = den + (shapes[c]/df_fisher.shape[0]) $*$   df_fisher[index[c]][col].var();
18.	End for c
19.	score = {‘feature’: col, ‘score’: num/den};
20.	scores.append(score);
21.	End for col
22.	Return Training_Set with selected features;
23.	End