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

Practice Questions

Q1
In regression analysis, 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 regression analysis, what does the term 'overfitting' refer to?
  • Step 1: Understand that regression analysis is a way to find relationships between variables.
  • Step 2: Know that a model is created using training data to make predictions.
  • Step 3: Realize that overfitting happens when the model learns the training data too perfectly.
  • Step 4: Recognize that this means the model picks up on random noise or errors in the training data.
  • Step 5: Understand that because of this, the model may not perform well on new, unseen data.
  • Step 6: Conclude that overfitting is bad because it makes the model less useful in real-world situations.
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