Plotting confusion matrix

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

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()

Confusion matrix python

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

Source: stackoverflow.com

Plotting confusion matrix

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

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