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Question

I have the below code to generate the confusion Matrix where it generates heatmap and accuracy_score

SOURCE

CODE

import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn import metrics

data = pd.read_excel(r"\Confusion Matrix.xlsx")

df = pd.DataFrame(data)
confusion_matrix = pd.crosstab(df['Actual'], df['Pred'], rownames=['Actual'], colnames=['Predicted'], margins = True)

sn.heatmap(confusion_matrix, annot=True)
plt.show()

accuray_score_in_percentage = accuracy_score(['Actual'], ['Pred'])
accuray_score_in_count = accuracy_score(['Actual'], ['Pred'], normalize=False)

print('The Precentage Accuracy is : ', accuray_score_in_percentage)
print('The Count of corrects are : ', accuray_score_in_count)

OUTPUT

From the above output you can see The Precentage Accuracy is : 0.0 and The Count of corrects are : 0 but it has to be The Precentage Accuracy is : 0.3 and The Count of corrects are : 3 . Can some one help me to modify the code so it shows me the correct Accuracy Scores .

Regards,

Bharath Vikas

Answer 1

You don't need the confusion matrix to compute accuracy.
Try :

accuray_score_in_percentage = accuracy_score(df['Actual'], df['Pred'])  
accuray_score_in_count = accuracy_score(df['Actual'], df['Pred'], normalize=False)

If you really want to use your confusion matrix, you can do :

accuray_score_in_percentage = (confusion_matrix.loc[0,0]+confusion_matrix.loc[1,1])/confusion_matrix.loc["All","All"]
accuray_score_in_count = confusion_matrix.loc[0,0]+confusion_matrix.loc[1,1]