Worksheet: Evaluating Models | Artificial Intelligence for Class 10 PDF Download

Multiple Choice Questions (MCQs)

Q.1: What is the primary purpose of the evaluation stage in the AI project cycle?
a. To collect data for the model
b. To assess the reliability of an AI model by comparing predictions with actual outcomes
c. To preprocess the data for modeling
d. To deploy the model in a real-world scenario  

Q.2: Why is it not recommended to use the training dataset for evaluating an AI model?
a. It reduces the accuracy of the model
b. The model may overfit and simply recall the training data
c. It increases the computation time
d. It makes the model incompatible with test data  

Q.3: In the context of the forest fire prediction model, what does a False Negative (FN) represent?
a. Predicting a fire when there is none
b. Correctly predicting a fire
c. Predicting no fire when a fire has occurred
d. Correctly predicting no fire  

Q.4: Which evaluation metric measures the percentage of correct predictions out of all observations?
a. Precision
b. Recall
c. Accuracy
d. F1 Score  

Q.5: What does the F1 Score represent in model evaluation?
a. The percentage of true positive cases
b. The balance between precision and recall
c. The total number of false negatives
d. The ratio of true negatives to false positives  

Fill in the Blanks

Q.6: The process of comparing a model’s predictions with actual outcomes using a test dataset is called ________.  

Q.7: The ________ matrix is used to record the comparison between prediction and reality in a model evaluation.  

Q.8: In a confusion matrix, the case where the model correctly predicts a positive outcome is called ________.  

Q.9: The evaluation metric that considers both True Positives and False Positives is called ________.  

Q.10: The formula for the F1 Score is a measure of balance between ________ and ________.  

True or False

Q.11: The confusion matrix is an evaluation metric that directly measures model performance.  

Q.12: Accuracy is calculated as the ratio of True Positives and True Negatives to the total number of observations.  

Q.13: High precision in a model indicates a high number of False Positives.  

Q.14: A False Positive in the forest fire scenario means the model predicts a fire when no fire has occurred.  

Q.15: The F1 Score is useful when both precision and recall are important for evaluating a model.  

Short Answer Questions

Q.16: Define model evaluation in one sentence.  

Q.17: Name two outcomes in the confusion matrix where the model’s prediction matches reality.  

Q.18: Explain the difference between Precision and Accuracy in the context of model evaluation.  

Q.19: What is a False Negative, and why might it be costly in the forest fire prediction scenario?  

Q.20: Why is the F1 Score considered a better evaluation metric than accuracy in some cases?  

Long Answer Questions

Q.21: Explain the role of the confusion matrix in evaluating an AI model, using the forest fire prediction scenario as an example.  

Q.22: Describe the steps to calculate Accuracy, Precision, Recall, and F1 Score for an AI model, using the confusion matrix components (TP, TN, FP, FN).  

Q.23: Discuss why a high accuracy might not indicate good model performance in the forest fire scenario, and suggest an alternative metric.  

Q.24: Provide an example of a scenario where a high False Negative cost is critical, and explain why minimizing False Negatives is important in that context.