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3 cards from this section. Quick, good the night before a test.
Define cross-validation in your own words.
Think about evaluating performance on unfamiliar datasets.
What is the purpose of k-fold cross-validation?
Consider why we might want to see how the model performs across different samples.
What is cross-validation primarily used for?
Think about why we evaluate model performance.
True or False: k-fold cross-validation can help with overfitting issues.
Consider what happens when a model struggles with unseen data.
Given a dataset of 1000 samples, describe how you would perform 10-fold cross-validation. What insights might you gain?
Think about how each fold contributes to a clearer picture of model performance.
Discuss how the choice of k in k-fold cross-validation influences the evaluation process, considering computational time and bias.
Reflect on the trade-offs involved in choosing k.
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