8 - Evaluation
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Practice Questions
Test your understanding with targeted questions
What is evaluation in AI?
💡 Hint: Think about what happens after the model is trained.
What is the training set used for?
💡 Hint: Recall which dataset is fed into the model to learn.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary purpose of evaluating an AI model?
💡 Hint: Consider the user experience with the model once it is deployed.
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.
1 more question available
Challenge Problems
Push your limits with advanced challenges
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.
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.
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Reference links
Supplementary resources to enhance your learning experience.
- What is Model Evaluation in Machine Learning?
- Confusion Matrix Explained
- Understanding Precision, Recall and F1 Score
- K-Fold Cross Validation in Machine Learning
- Model Evaluation Metrics in Machine Learning Explained
- Introduction to Evaluation Metrics in ML
- Practical Example of Classification Metrics