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()

sklearn plot confusion matrix

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import  plot_confusion_matrix
clf = LogisticRegression()
clf.fit(X_train,y_train)
disp = plot_confusion_matrix(clf,X_test,y_test,cmap="Blues",values_format='.3g')
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')

python confusion matrix without sklearn

import numpy as np

def compute_confusion_matrix(true, pred):
  '''Computes a confusion matrix using numpy for two np.arrays
  true and pred.

  Results are identical (and similar in computation time) to: 
    "from sklearn.metrics import confusion_matrix"

  However, this function avoids the dependency on sklearn.'''

  K = len(np.unique(true)) # Number of classes 
  result = np.zeros((K, K))

  for i in range(len(true)):
    result[true[i]][pred[i]] += 1

  return result

plot confusion matrix scikit learn

from sklearn import metrics
metrics.ConfusionMatrixDisplay.from_predictions(true_y, predicted_y).plot()

confusion matrix with labels sklearn

import pandas as pd
y_true = pd.Series([2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2])
y_pred = pd.Series([0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2])

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

confusion matrix with labels sklearn

Predicted  0  1  2  All
True                   
0          3  0  0    3
1          0  1  2    3
2          2  1  3    6
All        5  2  5   12