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3 cards from this section. Quick, good the night before a test.
Define supervised learning.
Think about how models learn from provided examples.
What is overfitting?
Consider what happens when a model tries to memorize rather than generalize.
What kind of learning uses labeled data?
Think about which type uses examples with answers provided.
True or False: Unsupervised learning is used for predicting future events.
Focus on the function of unsupervised learning.
Analyze a case where a model has high accuracy on training data but low accuracy on test data. What could be going wrong?
Consider the implications of model complexity.
Design a strategy to balance bias and variance in a simple decision tree model.
Focus on how to make the model generalize better.
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