Students can practice the best AI Class 10 MCQ Chapter 7 Evaluation Class 10 MCQ with Answers for exam preparation.
Class 10 AI Evaluation MCQ
MCQ on Evaluation Class 10
Class 10 Evaluation MCQ – Evaluation MCQ Class 10
Multiple Choice Questions:
Question 1.
With the help of which of the following, Prediction and Reality can be easily mapped together?
(A) Predictions
(B) Confusion Matrix
(C) Reality
(D) Recall
Answer:
(B) Confusion Matrix
Explanation: Prediction and Reality can be easily mapped together with the help of the Confusion Matrix. It allows us to understand the prediction results.
Question 2.
What will be the outcome, if the Prediction is “Yes” and it matches with the Reality? and what will be the outcomes Prediction is “Yes” and it does not match with the Reality?
(A) True Negative, False Negative
(B) True Negative, False Positive
(C) True Positive, True Negative
(D) True Positive, False Positive
Answer:
(D) True Positive, False Positive
Question 3.
Which of the following is not an example of an evaluation method?
(A) Precision
(B) Prediction
(C) Precision
(D) Recall
Answer:
(C) Precision
Explanation: Preparation is not an evaluation method.
Question 4.
…………… is defined as the fraction of positive cases that are correctly identified.
(A) Precision
(B) Accuracy
(C) Prediction
(D) Recall
Answer:
(D) Recall
Question 5.
F1 Score is I when:
(A) Recall and Precision is 100 %
(B) Recall is 100 %
(C) Precision
(D) Recall and Precision is 0.
Answer:
(A) Recall and Precision is 100 %
Question 6.
……….. is defined as the percentage of correct predictions out of all the observations.
(A) Prediction
(B) Accuracy
(C) Reality
(D) F1 Score
Answer:
(B) Accuracy
Explanation: Accuracy is defined as the percentage of correct predictions out of all observations.
Question 7.
Recall-Evaluation method is:
(A) defined as the fraction of positive cases that are correctly identified.
(B) defined as the percentage of true positive cases versus all the cases where the prediction is true.
(C) defined as the percentage of correct predictions out of all the observations.
(D) comparison between the prediction and reality
Answer:
(A) defined as the fraction of positive cases that are correctly identified.
Question 8.
F1 Score is the measure of the balance between
(A) Accuracy and Precision
(B) Precision and Recall
(C) Recall and Accuracy
(D) Recall and Reality
Answer:
(B) Precision and Recall
Explanation: The F1 score is an important evaluation metric, commonly used in classification tasks to evaluate the performance of a model. It combines precision and recall into a single value.
Question 9.
Which one of the following scenario result in a high false positive cost?
(A) viral outbreak
(B) forest fire
(C) flood
(D) spam filter
Answer:
(D) spam filter
Question 10.
Raunak was learning the conditions that make up the confusion matrix. He came across a scenario in which the machine that was supposed to predict an animal was always predicting not an animal. What is this condition called?
(A) False Positive
(B) True Positive
(C) False Negative
(D) True Negative
Answer:
(C) False Negative
Question 11.
Which two evaluation methods are used to calculate F1 Score?
(A) Precision and Accuracy
(B) Precision and Recall
(C) Accuracy and Recall
(D) Precision, F1 score
Answer:
(B) Precision and Recall
Question 12.
Which of the following statements is not true about overfitting models?
(A) This model learns the pattern and noise in the data to such extent that it harms the performance of the model on the new dataset
(B) Training result is very good and the test result is poor
(C) It interprets noise as patterns in the data
(D) The training accuracy and test accuracy both are low
Answer:
(D) The training accuracy and test accuracy both are low
Question 13.
Priya was confused with the terms used in the evaluation stage. Suggest her the term used for the percentage of correct predictions out of all the observations.
(A) Accuracy
(B) Precision
(C) Recall
(D) F1 Score
Answer:
(A) Accuracy
Explanation: Accuracy is defined as the percentage of correct predictions out of all observations.
Fill in the Blanks:
1. …………… is defined as the percentage of correct predictions out of all the observations.
Answer:
Accuracy
2. False Positive means the predicted value was
Answer:
Falsely Predicted
3. A model is said to have a good performance if the F1 Score for that model is …………… .
Answer:
High
4 …………… is the measure of a test’s accuracy.
Answer:
F-Measure
5. …………… is the stage of testing the model.
Answer:
Model Evaluation
6. The output given by the AI machine is known as …………… (Prediction/ Reality)
Answer:
Prediction
7. …………… is used to record the result of comparison between the prediction and reality. It is not an evaluation metric but a record which can help in evaluation.
Answer:
Confusion Matrix
Assertion & Reason Questions:
Directions: In the following questions, a statement of assertion (A) is followed by a statement of reason ( R ). Mark the correct choice as:
(A) Both assertion (A) and reason (R) are true and reason (R) is the correct explanation of assertion (A).
(B) Both assertion (A) and reason (R) are true but reason ( R ) is not the correct explanation of assertion (A).
(C) Assertion (A) is true but reason ( R ) is false.
(D) Both assertion and reason are false
Question 1.
Assertion (A): The output given by the AI model is known as reality.
Reason (R):The real scenario is known as Prediction.
Answer:
(D) Both assertion and reason are false