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• Privacy: Using real-world data for evaluation often involves sensitive personal information, raising concerns about
privacy. Measures should be in place to ensure that individual data is protected during the evaluation process.
• Data Protection: Protecting the data used in model evaluation is essential to prevent misuse or unauthorised access.
C. Competency-based/Application-based questions. 21 st Century #Critical Thinking
Skills
1. An AI model made the following predictions for Book Sales forecast. Calculate Accuracy, precision and recall for the
following confusion matrix:
Confusion Matrix True Positives True Negatives
Predicted Positive 50 40
Predicted Negative 12 10
Correction prediction
Ans. Accuracy = × 100%
Total Cases
(TP + TN)
= × 100%
(TP + TN + FP + FN)
50 + 10
= × 100%
50 + 10 + 40 + 12
60
= × 100%
112
= 53.5%
True Positive
Precision =
All Predicted Positive
TP
=
TP + FP
50
=
50 + 40
= 0.555 or 55.5%
True Positive
Recall =
True Positive + False Negative
50
=
50 + 12
50
=
62
= 0.806 or 80.6%
2. A company is developing a model to predict whether a customer will default on a loan. If the dataset is not split
properly into training and testing sets, what issues might arise? How would you ensure fair evaluation?
Ans. If the dataset is not split properly, the model might overfit, learning patterns from the training data but failing to
generalize to new data. This leads to poor performance on real-world predictions. To ensure fair evaluation, the dataset
should be split into a training set (for learning patterns) and a testing set (for evaluating generalisation). Sometimes, a
validation set is also used for hyperparameter tuning.
3. Identify which metric (Precision or Recall) is to be used in the following cases and why?
a. Email Spam Detection
b. Cancer Diagnosis
160 Artificial Intelligence Play (Ver 1.0)-X

