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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is evaluation in AI?
💡 Hint: Think about what happens after the model is trained.
Question 2
Easy
What is the training set used for?
💡 Hint: Recall which dataset is fed into the model to learn.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the primary purpose of evaluating an AI model?
💡 Hint: Consider the user experience with the model once it is deployed.
Question 2
True or False: Overfitting occurs when a model performs poorly on both training and testing data.
💡 Hint: Think about what happens when it learns too many details.
Solve 1 more question and get performance evaluation
Push your limits with challenges.
Question 1
You have a dataset where you trained an AI to categorize images into two classes: cats and dogs. After evaluation, you found your model’s accuracy is 85%, but the precision is only 70%. What's the potential issue with the model?
💡 Hint: Consider what high accuracy but low precision implies about false classifications.
Question 2
In a K-Fold cross-validation with k=5, if you trained the model and got different accuracy scores for each fold, what could you conclude about the model’s performance?
💡 Hint: Think about the importance of consistent performance across datasets.
Challenge and get performance evaluation