Class 10 Artificial Intelligence

Evaluating Models

51 Class 10 Artificial Intelligence questions on Evaluating Models from the CBSE board papers 2022–2026, each with its answer and a step-by-step solution.

Questions · page 3 of 3

1. Q41 CBSE 2023
   While evaluating a model's ​performance, recall parameter considers _________.

   1. (i)False ​positive
   2. (ii)True ​positive
   3. (iii)False negative
   4. (iv)​True ​negative Choose the ​correct ​option :

   1. (a)only (i)
   2. (b)(ii) and (iii)
   3. (c)(iii) and (iv)
   4. (d)(i) and (iv)
   Show answer

   (b) (ii) and (iii)

   Step-by-step solution →
2. Q42 CBSE 2023
   Two ​conditions ​when prediction ​matches ​with ​the reality ​are ​true ​positive and _________.
   Show answer

   True negative. Prediction and reality match in two cells of the confusion matrix: both Yes, which is the true positive, and both No, which is the true negative.

   Step-by-step solution →
3. Q43 CBSE 2023
   When ​the prediction ​matches ​the reality, the ​condition ​is ​termed ​as _________.
   Show answer

   True Positive - the model predicted Yes and the reality was also Yes, so the two agree. This is the sentence the Facilitator Handbook uses to introduce that cell. True Negative is the same agreement on No, and on the printed wording alone it is an equally acceptable filling.

   Step-by-step solution →
4. Q44 CBSE 2023
   Which of the following ​talks ​about how true the predictions ​are ​by ​any model ​?

   1. (a)Accuracy
   2. (b)Reliability
   3. (c)Recall
   4. (d)F1 score
   Show answer

   (a) Accuracy

   Step-by-step solution →
5. Q45 CBSE 2023
   Define Confusion Matrix.
   Show answer

   A confusion matrix is a table comparing predictions with reality, giving the four counts TP, FP, FN and TN.

   A confusion matrix is a table that compares what the model predicted with what actually happened, giving four counts: true positive, false positive, false negative and true negative. Accuracy, precision, recall and F1 are all calculated from it.

   Step-by-step solution →
6. Q46 CBSE 2023
   Recently the country was shaken up by a series ​of earthquakes which has done a huge damage ​to the people as well as the infrastructure. To address this ​issue, an ​AI model has ​been created which can ​predict if there is a chance ​of earthquake or ​not. The confusion matrix for the same is :

   Confusion Matrix	Reality: Yes	Reality: No
   Predicted: Yes	50	05
   Predicted: No	25	20

   1. (i)How many total cases are True Negative in the above scenario ?[1]
   2. (ii)Calculate precision, recall and F1 score.[3]
   Show answer

   True Negatives are 20; Precision is $0.91$, Recall is $0.67$ and F1 Score is $0.77$.

   (i) The true negative cases are the $20$ in which the model predicted No and the reality was also No. (ii) With $\text{TP}=50$, $\text{FP}=5$ and $\text{FN}=25$: Precision $=\frac{50}{50+5}=\frac{50}{55}=0.91$. Recall $=\frac{50}{50+25}=\frac{50}{75}=0.67$. F1 Score $=\frac{2\times \frac{50}{55}\times \frac{50}{75}}{\frac{50}{55}+\frac{50}{75}}=\frac{100}{130}=0.77$.

   Step-by-step solution →
7. Q47 CBSE 2022
   With ​reference to evaluation process of ​understanding the reliability ​of ​any AI model, ​define ​the term ​True Positive.
   Show answer

   True Positive is the case where the model predicts Yes and the reality is also Yes.

   True Positive is the case where the model predicts Yes (positive) and the reality is also Yes, so the prediction matches the reality; for example, a forest fire has broken out and the model predicts a fire.

   Step-by-step solution →
8. Q48 CBSE 2022
   What is F1 score ?
   Show answer

   F1 score is the measure of balance between precision and recall.

   F1 score is the measure of balance between precision and recall: $\text{F1}=\frac{2\times \text{Precision}\times \text{Recall}}{\text{Precision}+\text{Recall}}$.

   Step-by-step solution →
9. Q49 CBSE 2022
   What ​is ​the ​purpose ​of Evaluation stage of AI project ​cycle ​? ​Discuss ​briefly.
   Show answer

   It checks how reliable the trained model is by comparing its outputs on unseen test data with actual answers, so the best model is chosen.

   The Evaluation stage checks how reliable the trained model is. Test data the model has not seen is fed into it and its outputs are compared with the actual answers, using metrics such as accuracy, precision, recall and F1, so the best model can be chosen and its future performance judged.

   Step-by-step solution →
10. Q50 CBSE 2022
    With reference to evaluation stage ​of AI project ​cycle, explain the ​term Accuracy. Also ​give the formula ​to ​calculate it.
    Show answer

    Accuracy is the percentage of correct predictions out of all observations: $\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}\times 100\%$.

    Accuracy is the percentage of correct predictions out of all the observations; a prediction is correct when it matches reality (True Positives and True Negatives). $\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}\times 100\%$.

    Step-by-step solution →
11. Q51 CBSE 2022
    Traffic Jams ​have become a common part of our lives now-a-days. Living in an urban area means you have to ​face ​traffic ​each and every time ​you get out on the road. Mostly, school students opt for buses to go to school. Many times the bus gets late due to such jams and students are not able to reach their school on time. ​Thus, an AI model is created to ​predict explicitly if there would be a traffic jam on their way to school or not. The confusion matrix ​for the same is

    The Confusion Matrix	Actual : 1	Actual : 0
    Predicted : 1	50	50
    Predicted : 0	0	0

    1. (i)Explain the process of calculating F1 score for the given problem.[4]
    Show answer

    Take 1 (traffic jam) as positive: $\text{TP}=50$, $\text{FP}=50$, $\text{FN}=0$, $\text{TN}=0$. Precision $=\frac{\text{TP}}{\text{TP}+\text{FP}}=\frac{50}{100}=0.5$. Recall $=\frac{\text{TP}}{\text{TP}+\text{FN}}=\frac{50}{50}=1$. F1 Score $=\frac{2\times \text{Precision}\times \text{Recall}}{\text{Precision}+\text{Recall}}=\frac{2\times 0.5\times 1}{0.5+1}=\frac{2}{3}=0.667$.

    Step-by-step solution →