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Where,
Total Correct Predictions = True Positive (TP) + True Negative (TN)
Total Predictions = True Positive (TP) + True Negative (TN) + False Positive (FP) + False Negative (FN)
A prediction is said to be correct if it matches reality. Here we have two conditions in which the Prediction matches
with the Reality, i.e., True Positive and True Negative.
For example, in a model of predicting whether the credit card transaction is fraudulent or not, the confusion matrix
is as follows:
Predicted
Confusion Matrix
Yes No
Yes 15 14
Actual
No 12 10
Total Correct Predictions = TP+TN
= 15+10
= 25
Total Predictions = TP+TN+FP+FN
= 15+10+12+14
= 51
Total no. of correct predictions
Classification Accuracy = × 100
Total no. of predictions
(TP + TN)
= × 100
(TP + TN + FP + FN)
25
= × 100
51
= 49%
Can we use Accuracy all the time?
It is suitable wherever the dataset is balanced, which means the positive and negative classes are roughly equal, that
is a rare occurrence, and that all predictions and prediction errors are equally important, which is often not the case.
For example, Calculating the accuracy of the classifier model, that predicts whether a student will pass a test
(Yes) or not pass a test (No). It classifies the input into two classes Yes and No. Let's, calculate the accuracy of the
classifier model and construct the confusion matrix for the model. Here,
• Total test data is 1000.
• Actual values are 900 Yes and 100 No (Unbalanced dataset).
• It is a faulty model which, irrespective of any input, will give a prediction as Yes.
• Calculate the classification accuracy of this model.
To prepare the classification accuracy of this model follow the given steps:
Step 1 Construct the Actual value vs Predicted value table. Consider Yes as the positive class and No as the
negative class.
Predicted Value Actual Value
Evaluating Models 151

