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Class 10 Exam  >  Class 10 Tests  >  Test: - Class 10 MCQ

Test: - Class 10 MCQ


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10 Questions MCQ Test - Test:

Test: for Class 10 2024 is part of Class 10 preparation. The Test: questions and answers have been prepared according to the Class 10 exam syllabus.The Test: MCQs are made for Class 10 2024 Exam. Find important definitions, questions, notes, meanings, examples, exercises, MCQs and online tests for Test: below.
Solutions of Test: questions in English are available as part of our course for Class 10 & Test: solutions in Hindi for Class 10 course. Download more important topics, notes, lectures and mock test series for Class 10 Exam by signing up for free. Attempt Test: | 10 questions in 10 minutes | Mock test for Class 10 preparation | Free important questions MCQ to study for Class 10 Exam | Download free PDF with solutions
Test: - Question 1
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What is the primary purpose of evaluation in the AI project cycle?

Detailed Solution for Test: - Question 1
The primary purpose of evaluation in the AI project cycle is to check the reliability of the AI model. It helps determine if the model is performing as expected and if it can make accurate predictions or classifications based on the test dataset.
Test: - Question 2
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Why is it not recommended to use the same data for both building and evaluating an AI model?

Detailed Solution for Test: - Question 2
It is not recommended to use the same data for both building and evaluating an AI model because doing so can lead to overfitting. Overfitting occurs when the model memorizes the training set and performs well on it but fails to generalize to new, unseen data.
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Test: - Question 3
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In the context of AI evaluation, what is a "True Negative"?

Detailed Solution for Test: - Question 3
A "True Negative" occurs when the model predicts "No," and the reality is also "No." In other words, the model correctly identifies a negative condition.
Test: - Question 4
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Why is a high Precision value important in some AI applications?
Detailed Solution for Test: - Question 4
A high Precision value is important in some AI applications because it ensures a low False Positive rate, reducing costly errors. For example, in medical diagnosis, a high Precision means fewer false alarms, which can be critical for patient health.
Test: - Question 5
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In which scenario would a high F1 Score be considered ideal?
Detailed Solution for Test: - Question 5
A high F1 Score is considered ideal when both Precision and Recall are high. This indicates that the model has a good balance between making accurate positive predictions (high Precision) and correctly identifying positive cases (high Recall).
Test: - Question 6
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Why is it important to consider both Precision and Recall in evaluating an AI model?
Detailed Solution for Test: - Question 6
It is important to consider both Precision and Recall in evaluating an AI model because they focus on different aspects of model performance. Precision deals with false positives, while Recall deals with false negatives. A good model should strike a balance between minimizing both types of errors.
Test: - Question 7
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What is the range of values for the F1 Score?
Detailed Solution for Test: - Question 7
The range of values for the F1 Score is from 0 to 1. It is a decimal value that indicates the balance between Precision and Recall, with 1 being the ideal value.
Test: - Question 8
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In which case would a False Negative be more costly?
Detailed Solution for Test: - Question 8
A False Negative would be more costly in the case of predicting viral outbreaks. If the model fails to detect a viral outbreak (False Negative), it can have severe consequences, affecting the health and lives of many people.
Test: - Question 9
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What is the F1 Score?
Detailed Solution for Test: - Question 9
The F1 Score is a measure of the balance between Precision and Recall. It takes into account both metrics to provide a single value that reflects the overall performance of the model.
Test: - Question 10
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What does the precision evaluation metric primarily take into account?
Detailed Solution for Test: - Question 10
Precision primarily takes into account True Positives and False Positives. It measures the accuracy of positive predictions made by the model.
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