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3 cards from this section. Quick, good the night before a test.
Define supervised learning.
Think about what supervised implies.
What does overfitting mean?
Consider how it performs on new data.
What is supervised learning?
Focus on the meaning of 'supervised'.
True or False: Overfitting refers to a model ignoring the training data.
Remember what overfitting entails.
You are developing a model for predicting house prices. You notice that your model is overfitting the training data. Suggest a method to mitigate this.
Think about the relationship between training data and unseen data.
In a classification problem, you have an imbalanced dataset. How might this affect the precision and recall of your model?
Consider how the model's focus might shift in the presence of bias in the datasets.
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