Practice Evaluation Methodologies Of Ai Models (12) - Evaluation Methodologies of AI Models

Practice: Evaluation Methodologies of AI Models

Lesson 12Evaluation Methodologies of AI Models

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11 questions

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6 questions · about 4 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does a confusion matrix show?
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Think about how we compare correct versus incorrect predictions.
Question 2.
Define accuracy in the context of model evaluation.
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Consider the total number of correct predictions.
3Question 3.
What does a confusion matrix compare?
- Actual vs. predicted values
- Training vs. testing data
- Model parameters vs. performance
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It's primarily focused on predictions.
4Question 4.
True or False: Accuracy is always a reliable measure of model performance.
- True
- False
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Consider scenarios where accuracy may not reflect true performance.
Question 5.
Suppose you have an imbalanced dataset with 90% negatives and 10% positives. You want to evaluate the model's performance strictly for the positive class. Which metric would you rely on and why?
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Think about the importance of false positives in your evaluation.
Question 6.
Design a small experiment where you apply both train-test split and k-fold cross-validation on the same dataset. Discuss the findings regarding model performance using these two methods.
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Consider how data partitions impact learning.

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