Unit 3: Evaluating Models Class 10 AI Question Answers
Class 10 AI NCERT Solutions | Unit 3 | Evaluating Models
SUMMARY OF UNIT 3:
1. Model evaluation is the process of measuring the performance of an AI model.
2. Model evaluation helps identify the strengths and weaknesses of an AI model.
3 A train-test split divides the dataset into training data and testing data.
4. Training data is used to train the AI model.
5. Testing data is used to evaluate the model on unseen data.
6. Using only training data for evaluation can lead to overfitting.
7. Accuracy measures how many predictions made by the model are correct.
8. Error is the difference between the predicted value and the actual value.
9. Classification is the process of placing data into different categories.
10. A confusion matrix is used to evaluate the performance of a classification model.
11. A confusion matrix contains True Positive, True Negative, False Positive, and False Negative values.
12. True Positive (TP) means the model correctly predicts the positive class.
13. True Negative (TN) means the model correctly predicts the negative class.
14. False Positive (FP) means the model incorrectly predicts a negative case as positive.
15. False Negative (FN) means the model incorrectly predicts a positive case as negative.
16. Classification accuracy is the ratio of correct predictions to the total number of predictions.
17. Precision measures how many predicted positive cases are actually positive.
18. High precision means the model produces fewer false positive predictions.
19. Recall measures how many actual positive cases are correctly identified by the model.
20. High recall means the model produces fewer false negative predictions.
21. Recall is also known as Sensitivity or True Positive Rate.
22. F1 Score combines Precision and Recall into a single evaluation metric.
23. F1 Score is useful when both false positives and false negatives are equally important.
24. Accuracy is preferred for balanced datasets with equal class distribution.
25. Precision is important when false positive predictions must be minimized.
26. Recall is important when false negative predictions must be minimized.
27. Ethical AI model evaluation requires fairness, transparency, and accountability.
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Unit 3: Evaluating Models Class 10 AI Question Answers
Multiple Choice Questions
Q1. In a medical test for a rare disease, out of 1000 people tested, 50 actually have the disease while 950 do not. The test correctly identifies 40 out of the 50 people with the disease as positive, but it also wrongly identifies 30 of the healthy individuals as positive. What is the accuracy of the test?
A) 97%
B) 90%
C) 85%
D) 70%
Q2. A student solved 90 out of 100 questions correctly in a multiple-choice exam. What is the error rate of the student’s answers?
A) 10%
B) 9%
C) 8%
D) 11%
Q3. In a spam email detection system, out of 1000 emails received, 300 are spam. The system correctly identifies 240 spam emails as spam, but it also marks 60 legitimate emails as spam. What is the
precision of the system?
A) 80%
B) 70%
C) 75%
D) 90%
Q4. In a binary classification problem, a model predicts 70 instances as positive out of which 50 are actually positive. What is the recall of the model?
A) 50%
B) 70%
C) 80%
D) 100%
Q5. In a sentiment analysis task, a model correctly predicts 120 positive sentiments out of 200 positive instances. However, it also incorrectly predicts 40 negative sentiments as positive. What is the F1 score of the model?
A) 0.8
B) 0.75
C) 0.72
D) 0.82
Q6. A medical diagnostic test is designed to detect a certain disease. Out of 1000 people tested, 100 have the disease, and the test identifies 90 of them correctly. However, it also wrongly identifies 50 healthy people as having the disease. What is the precision of the test?
A) 90%
B) 80%
C) 70%
D) 60%
Q7. A teacher’s marks prediction system predicts the marks of a student as 75, but the actual marks obtained by the student are 80. What is the absolute error in the prediction?
A) 5
B) 10
C) 15
D) 2
Q8. The goal when evaluating an AI model is to:
A) Maximize error and minimize accuracy
B) Minimize error and maximize accuracy
C) Focus solely on the number of data points used
D) Prioritize the complexity of the model
Q9. A high F1 score generally suggests:
A) A significant imbalance between precision and recall
B) A good balance between precision and recall
C) A model that only performs well on specific data points
D) The need for more training data
Q10. How is the relationship between model performance and accuracy described?
A) Inversely proportional
B) Not related
C) Directly proportional
D) Randomly fluctuating
Class 10 AI NCERT Solutions
Reflection Time:
Q1. What will happen if you deploy an AI model without evaluating it with known test set data?
Q2. Do you think evaluating an AI model is that essential in an AI project cycle?
Q3. Explain train-test split with an example.
Q4. “Understanding both error and accuracy is crucial for effectively evaluating and improving AI models.” Justify this statement.
Q5. What is classification accuracy? Can it be used all times for evaluating AI models?
Q6. Identify which metric (Precision or Recall) is to be used in the following cases and why?
a) Email Spam Detection
b) Cancer Diagnosis
c) Legal Cases(Innocent until proven guilty)
d) Fraud Detection
e) Safe Content Filtering (like Kids YouTube)
Assertion and reasoning-based questions:
Q1. Assertion: Accuracy is an evaluation metric that allows you to measure the total number of predictions a model gets right.
Reasoning: The accuracy of the model and performance of the model is directly proportional, and hence better the performance of the model, the more accurate are the predictions.
Choose the correct option:
(a) Both A and R are true and R is the correct explanation for A
(b) Both A and R are true and R is not the correct explanation for A
(c) A is True but R is False
(d) A is false but R is True
Q2. Assertion: The sum of the values in a confusion matrix’s row represents the total number of instances for a given actual class.
Reasoning: This enables the calculation of class-specific metrics such as precision and recall, which are essential for evaluating a model’s performance across different classes.
Choose the correct option:
(a) Both A and R are true and R is the correct explanation for A
(b) Both A and R are true and R is not the correct explanation for A
(c) A is True but R is False
(d) A is false but R is True
Case study-based questions:| Evaluating Models Class 10 AI Question Answers
Q2. Examine the following case studies. Draw the confusion matrix and calculate metrics such as accuracy, precision, recall, and F1-score for each one of them.
a. Case Study 1:
A spam email detection system is used to classify emails as either spam (1) or not spam (0). Out of 1000 emails:
- True Positives(TP): 150 emails were correctly classified asspam.
- False Positives(FP): 50 emails were incorrectly classified asspam.
- True Negatives(TN): 750 emails were correctly classified as not spam.
- False Negatives(FN): 50 emails were incorrectly classified as not spam.
b. Case Study 2:
A credit scoring model is used to predict whether an applicant is likely to default on a loan (1) or not (0). Out of 1000 loan applicants:
True Positives(TP): 90 applicants were correctly predicted to default on the loan.
False Positives(FP): 40 applicants were incorrectly predicted to default on the loan.
True Negatives(TN): 820 applicants were correctly predicted not to default on the loan
False Negatives (FN): 50 applicants were incorrectly predicted not to default on the loan.
Calculate metrics such as accuracy, precision, recall, and F1-score.
c. Case Study 3:
A fraud detection system is used to identify fraudulent transactions(1) from legitimate ones(0). Out of 1000 transactions:
True Positives(TP): 80 transactions were correctly identified as fraudulent.
False Positives(FP): 30 transactions were incorrectly identified as fraudulent.
True Negatives(TN): 850 transactions were correctly identified as legitimate.
False Negatives(FN): 40 transactions were incorrectly identified as legitimate.
Calculate metrics such as accuracy, precision, recall, and F1-score.
d. Case Study 4:
A medical diagnosis system is used to classify patients as having a certain disease (1) or not having it (0). Out of 1000 patients:
True Positives(TP): 120 patients were correctly diagnosed with the disease.
False Positives(FP): 20 patients were incorrectly diagnosed with the disease.
True Negatives(TN): 800 patients were correctly diagnosed as not having the disease.
False Negatives(FN): 60 patients were incorrectly diagnosed as not having the disease.
Calculate metrics such as accuracy, precision, recall, and F1-score.
e. Case Study 5:
An inventory management system is used to predict whether a product will be out of stock (1) or not (0) in the next month. Out of 1000 products:
True Positives (TP): 100 products were correctly predicted to be out of stock.
False Positives (FP): 50 products were incorrectly predicted to be out of stock.
True Negatives (TN): 800 products were correctly predicted not to be out of stock.
False Negatives(FN): 50 products were incorrectly predicted not to be out of stock.
Calculate metrics such as accuracy, precision, recall, and F1-score.
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This Unit 3: Evaluating Models Class 10 AI Notes covers the following topics
What is Train-test split? Why do we need to do Train-test split? Accuracy and Error. What is Classification?F1 Score, Classification metrics, Confusion matrix, Accuracy from Confusion matrix, Precision from Confusion matrix, Recall from Confusion matrix, Bias, Accountability, Transparency
Important links of Class 10 AI (Artificial Intelligence)
Unit -1-Revisiting AI Project Cycle & Ethical Frameworks for AI – NOTES
Unit -1-Revisiting AI Project Cycle & Ethical Frameworks for AI – Question Answers
Unit 2. Advanced concepts of Modeling in AI – NOTES
Unit 2. Advanced concepts of Modeling in AI – Question Answers
Unit 3. Evaluating Models Class 10 AI – NOTES
Important links of Class IX (IT-402)
Unit 1 : Introduction to IT–ITeS Industry BOOK SOLUTIONS
Unit 1 : Introduction to IT–ITeS Industry NOTES
Unit 1 : Introduction to IT-ITeS MCQ
Unit 3 : Digital Documentation NOTES
Unit 3 : Digital Documentation BOOK SOLUTIONS
Unit 3 : Digital Documentation MCQ
Unit 4 : Electronic Spreadsheet BOOK SOLUTIONS
Unit 4 : Electronic Spreadsheet MCQ
Unit 5 : Digital Presentation MCQ
Important links of Class X (IT – 402)
Unit 1: Digital Documentation (Advanced) using LibreOffice Writer
Chapter 1. Introduction to Styles – NOTES
Chapter 1. Introduction to Styles – Question Answers
Chapter 2. Working with Images – NOTES
Chapter 2. Working with Images – Question Answers
Chapter 3. Advanced features of Writer – NOTES
Chapter 3. Advanced features of Writer – Question Answers
Unit 2: Electronic Spreadsheet (Advanced) using LibreOffice Calc
Chapter 4. Analyse Data using Scenarios and Goal Seek – NOTES
Chapter 4. Analyse Data using Scenarios and Goal Seek – Question Answers
Chapter 5. Using Macros in Spreadsheet – NOTES
Chapter 5. Using Macros in Spreadsheet – Question Answers
Chapter 6. Linking Spreadsheet Data – NOTES
Chapter 6. Linking Spreadsheet Data – Question Answers
Chapter 7. Share and Review a Spreadsheet – NOTES
Chapter 7. Share and Review a Spreadsheet – Question Answers
Unit 3: Database Management system using LibreOffice Base
Chapter 8. Introduction to DBMS – NOTES
Chapter 8. Introduction to DBMS – Question Answers
Chapter 9. Starting with LibreOffice Base – NOTES
Chapter 9. Starting with LibreOffice Base – Question Answers
Chapter 10. Working with Multiples Tables – NOTES
Chapter 10. Working with Multiples Tables – Question Answers
Chapter 11. Queries in LibreOffice Base – NOTES
Chapter 11. Queries in LibreOffice Base – Question Answers
Chapter 12. Forms and Reports – NOTES
Chapter 12. Forms and Reports – Question Answers
Unit 4: Prevent Accident and Emergencies
Chapter 13. Health, Safety and Security at Workplace – NOTES
Chapter 13. Health, Safety and Security at Workplace – Question Answers
Chapter 14. Workplace Safety Measures – NOTES
Chapter 14. Workplace Safety Measures – Question Answers
Chapter 15. Prevent Accidents and Emergencies – NOTES
Chapter 15. Prevent Accidents and Emergencies – Question Answers
Important links of Class X (IT – 402)
UNIT 1: DIGITAL DOCUMENTATION (ADVANCED) MCQ
UNIT-2: ELECTRONIC SPREADSHEET (ADVANCED) MCQ
UNIT-3 RELATIONAL DATABASE MANAGEMENT SYSTEMS (BASIC) MCQ
UNIT-4 WEB APPLICATIONS AND SECURITY MCQ