confusion matrix python

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Mateo
08 Nov 2018
1By definition, entry i,j in a confusion matrix is the number of 
2observations actually in group i, but predicted to be in group j. 
3Scikit-Learn provides a confusion_matrix function:
4
5from sklearn.metrics import confusion_matrix
6y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
7y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
8confusion_matrix(y_actu, y_pred)
9# Output
10# array([[3, 0, 0],
11#        [0, 1, 2],
12#        [2, 1, 3]], dtype=int64)
source
Castiel
18 Apr 2020
1from sklearn.metrics import confusion_matrix
2cm = confusion_matrix(y_test, y_predicted)
3cm
4# after creating the confusion matrix, for better understaning plot the cm.
5import seaborn as sn
6plt.figure(figsize = (10,7))
7sn.heatmap(cm, annot=True)
8plt.xlabel('Predicted')
9plt.ylabel('Truth')
source
Raj
05 Sep 2019
1df_confusion = pd.crosstab(y_actu, y_pred, rownames=['Actual'], colnames=['Predicted'], margins=True)
2
source
Britney
16 Jun 2019
1import numpy as np
2
3currentDataClass = [1, 3, 3, 2, 5, 5, 3, 2, 1, 4, 3, 2, 1, 1, 2]
4predictedClass = [1, 2, 3, 4, 2, 3, 3, 2, 1, 2, 3, 1, 5, 1, 1]
5
6def comp_confmat(actual, predicted):
7
8    classes = np.unique(actual) # extract the different classes
9    matrix = np.zeros((len(classes), len(classes))) # initialize the confusion matrix with zeros
10
11    for i in range(len(classes)):
12        for j in range(len(classes)):
13
14            matrix[i, j] = np.sum((actual == classes[i]) & (predicted == classes[j]))
15
16    return matrix
17
18comp_confmat(currentDataClass, predictedClass)
19
20array([[3., 0., 0., 0., 1.],
21       [2., 1., 0., 1., 0.],
22       [0., 1., 3., 0., 0.],
23       [0., 1., 0., 0., 0.],
24       [0., 1., 1., 0., 0.]])
25
26
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