scikit 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()

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)

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

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