/Python/How to get confusion matrix in python

How to get confusion matrix in python

Confusion matrix python

from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, y_pred)
sns.heatmap(conf_mat, square=True, annot=True, cmap='Blues', fmt='d', cbar=False)

Confusion matrix python

By definition, entry i,j in a confusion matrix is the number of 
observations actually in group i, but predicted to be in group j. 
Scikit-Learn provides a confusion_matrix function:

from sklearn.metrics import confusion_matrix
y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
confusion_matrix(y_actu, y_pred)
# Output
# array([[3, 0, 0],
#        [0, 1, 2],
#        [2, 1, 3]], dtype=int64)

Source: scikit-learn.org

Confusion matrix python

df_confusion = pd.crosstab(y_actu, y_pred, rownames=['Actual'], colnames=['Predicted'], margins=True)

Source: stackoverflow.com

How to get confusion matrix in python

from sklearn.metrics import confusion_matrix
conf_mat = confusion_matrix(y_test, y_pred)

Compute confusion matrix using python

import numpy as np

currentDataClass = [1, 3, 3, 2, 5, 5, 3, 2, 1, 4, 3, 2, 1, 1, 2]
predictedClass = [1, 2, 3, 4, 2, 3, 3, 2, 1, 2, 3, 1, 5, 1, 1]

def comp_confmat(actual, predicted):

    classes = np.unique(actual) # extract the different classes
    matrix = np.zeros((len(classes), len(classes))) # initialize the confusion matrix with zeros

    for i in range(len(classes)):
        for j in range(len(classes)):

            matrix[i, j] = np.sum((actual == classes[i]) & (predicted == classes[j]))

    return matrix

comp_confmat(currentDataClass, predictedClass)

array([[3., 0., 0., 0., 1.],
       [2., 1., 0., 1., 0.],
       [0., 1., 3., 0., 0.],
       [0., 1., 0., 0., 0.],
       [0., 1., 1., 0., 0.]])

Source: stackoverflow.com

Confusion matrix python code

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_predicted)
cm
# after creating the confusion matrix, for better understaning plot the cm.
import seaborn as sn
plt.figure(figsize = (10,7))
sn.heatmap(cm, annot=True)
plt.xlabel('Predicted')
plt.ylabel('Truth')

-1

Similar codes

0 vote176 views1 answer
Delete history django simple
Python Django
0 vote168 views1 answer
Import external script in django views
Python Django
1 vote133 views1 answer
Replace url with text python
Python
0 vote139 views1 answer
Filter numbers with bounds filter_bounds python
Python
3 votes129 views1 answer
Python cmd to remove file
Python
0 vote138 views1 answer
Python Sorted Word frequency count
Python
0 vote134 views1 answer
What is t in python
Python
1 vote158 views1 answer
Python sympy solve equation equal to 0
Python
8 votes130 views2 answers
Python f-string padding
Python
0 vote146 views1 answer
Copy files to a directory using text file
Python