Page 251 - AI Ver 3.0 class 10_Flipbook
P. 251
Assertion and Reasoning questions.
Direction: Questions 2-4, consist of two statements – Assertion (A) and Reasoning (R). Answer these questions by selecting
the appropriate option given below:
a. Both A and R are correct, and R is the correct explanation of A.
b. Both A and R are correct, but R is NOT the correct explanation of A.
c. A is correct, but R is incorrect.
d. A is incorrect, but R is correct.
2. Assertion (A): A model with high accuracy always performs well on all types of classification problems.
Reasoning(R): Accuracy is a reliable metric for evaluating model performance in all scenarios.
3. Assertion (A): Bias in training data can lead to unfair predictions in AI models.
Reasoning (R): If the training dataset lacks diversity, the model may learn and reinforce existing biases.
4. Assertion (A): Accuracy is an evaluation metric that allows you to measure the total number of predictions a model
gets right.
Reasoning (R): 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. [CBSE Handbook]
In Life 21 st Century #Communication
#Initiative
Skills
How can evaluation techniques be applied to assess the effectiveness of educational programs or courses? Share
your examples of specific methods used to gather data and measure learning outcomes with the class.
Deep Thinking
21 st Century #Critical Thinking
Skills
An educational institution is implementing a new online learning platform to enhance student engagement. The
administrators want to evaluate its effectiveness and identify areas for improvement. What quantitative methods
could be employed to measure student participation and learning outcomes on the new platform? Share your
thoughts with the class.
21 st Century #Technology Literacy
Skills
Lab
1. Explore the internet to find some scenarios related to natural disasters. Take any one out of them and
make prediction-reality comparison. Draw a confusion matrix for it to show prediction results.
2. For the scenario in the above question, make evaluation through its parameters_ Accuracy, Precision and
Recall.
3. Calculate the following measures for the given confusion matrix - Accuracy, Precision, Recall and F1 Score.
Confusion Matrix True Positive True Negative
Predictive Positive 100 45
Predicted Negative 65 320
Evaluating Models 249

