Recall & Review
beginner
What is the main goal of model comparison in machine learning?
The main goal is to find the best model that makes the most accurate predictions on new, unseen data.
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beginner
What is cross-validation and why is it used in model comparison?
Cross-validation splits data into parts to train and test models multiple times. It helps check how well a model will perform on new data and reduces bias from a single train-test split.
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beginner
Explain the difference between training error and test error.
Training error measures mistakes on data the model learned from. Test error measures mistakes on new data. Test error shows how well the model generalizes to unseen data.
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intermediate
What is overfitting and how does it affect model comparison?
Overfitting happens when a model learns the training data too well, including noise, causing poor performance on new data. It can make a model look good during training but bad during testing.
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beginner
Name two common metrics used to compare classification models.
Accuracy and F1-score are common metrics. Accuracy measures overall correct predictions. F1-score balances precision and recall, useful when classes are imbalanced.
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Which method helps estimate model performance by splitting data multiple times?
What does a high training accuracy but low test accuracy usually indicate?
Which metric is best when classes are imbalanced?
Why is it important to compare models on test data rather than training data?
Which strategy helps reduce bias in model performance estimates?
Describe how cross-validation works and why it is useful for comparing models.
Explain the difference between overfitting and underfitting and how they affect model comparison.