Answers for "plot confusion matrix python"

Python

sklearn plot confusion matrix

import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, plot_confusion_matrix

clf = # define your classifier (Decision Tree, Random Forest etc.)
clf.fit(X, y) # fit your classifier

# make predictions with your classifier
y_pred = clf.predict(X) 
        
# optional: get true negative (tn), false positive (fp)
# false negative (fn) and true positive (tp) from confusion matrix
M = confusion_matrix(y, y_pred)
tn, fp, fn, tp = M.ravel() 

# plotting the confusion matrix
plot_confusion_matrix(clf, X, y)
plt.show()

Posted by: Guest on May-14-2021

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)

Posted by: Guest on April-24-2020

Source

import sklearn.metrics from plot_confusion_matrix

from sklearn.metrics import plot_confusion_matrix

Posted by: Guest on October-17-2020

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')

Posted by: Guest on April-08-2021

confusion matrix python

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

Posted by: Guest on January-18-2021

Source

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.]])

Posted by: Guest on December-20-2020

Source

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