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• Accuracy: If the model predicts, that the loan is approved or not approved, and it matches the reality, then that
means the model is accurate for that dataset.
Understanding both error and accuracy is crucial for effectively evaluating and improving AI models.
Prediction
Approval of Loan AI Model Loan Approved
Approved
–
Actual
Loan Approved
Understanding both error and accuracy is crucial for effectively evaluating and improving AI models. From the
above example we can say,
• The focus is to maximise the accuracy in the performance of the model and minimise the error.
• In real life scenarios, the accuracy is dependent on the data, if the dataset is not realistic, the best models
may make mistakes. For example, in approval of loan, a model with slightly lower accuracy but the focus is on
avoiding incorrectly identifying a right applicant may be preferred by the banks.
• Selection of the model with the balance of accuracy and error depends upon the task and its requirements by
the model
Task 21 st Century #Information Literacy
Skills
#Critical Thinking
Find the accuracy of the AI model mathematically.
Calculate the accuracy of AI model which predicts the salary of employees:
• Using the given formulae complete the given table:
Error Absolute = ABS(Actual Value -Predicted Value)
Error Rate = (Error/Actual value)
Accuracy = (1-Error Rate)
Accuracy% = (Accuracy*100)%
Predicted Salary Actual Salary Error Absolute Error Rate Accuracy Accuracy%
47,000 45,000 2,000 2,000/45,000 1-0.044=0.956 0.956*100=95.6%
=0.044
56,000 56,500 500 500/56,500 1-0.008=0.992 0.992*100=99.2%
=0.008
45,000 45,500 500
38,000 37,000 1,000
65,000 67,000 2000
• Accuracy of the AI model is the mean accuracy of all five samples.
(Sum of Accuracy%)
Accuracy of the AI model =
Total number of samples
Complete the above table and find the accuracy of the AI model.
Evaluating Models 145

