Confusion_matrix_multiclass.py

# confusion matrix in sklearn
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

# actual values
actual = ['a','b','c','a','b','c','a','b','c']
# predicted values
predicted = ['a','c','b','b','b','c','a','c','c']

# confusion matrix
matrix = confusion_matrix(actual,predicted, labels=['a','b','c'])
print('Confusion matrix : \n',matrix)

# outcome values order in sklearn
c00, c01, c02, c10, c11, c12, c20, c21, c22 = confusion_matrix(actual,predicted,labels=['a','b','c']).reshape(-1)

# values for class 'a'
tp1 = c00
fp1 = c10 + c20
fn1 = c01 + c02
tn1 = c11 + c12 + c21 + c22
print('Values for class "a" :','\n',tp1,fp1,fn1,tn1)

# classification report for precision, recall f1-score and accuracy
matrix = classification_report(actual,predicted,labels=['a','b','c'])
print('Classification report : \n',matrix)