In linear regression, what does the term 'overfitting' refer to?

Practice Questions

Q1
In linear regression, what does the term 'overfitting' refer to?
  1. The model performs well on training data but poorly on unseen data
  2. The model is too simple to capture the underlying trend
  3. The model has too few features
  4. The model is perfectly accurate

Questions & Step-by-Step Solutions

In linear regression, what does the term 'overfitting' refer to?
  • Step 1: Understand that linear regression is a method used to predict outcomes based on input data.
  • Step 2: Know that a model is trained using a set of data called 'training data'.
  • Step 3: Realize that during training, the model tries to find patterns in the data.
  • Step 4: Overfitting happens when the model learns not just the patterns, but also the random noise in the training data.
  • Step 5: When a model is overfitted, it performs very well on the training data but poorly on new, unseen data.
  • Step 6: This is because the model is too complex and has memorized the training data instead of generalizing from it.
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