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What does the term 'overfitting' refer to in machine learning?

Practice Questions

Q1
What does the term 'overfitting' refer to in machine learning?
  1. A model that performs well on training data but poorly on unseen data
  2. A model that generalizes well to new data
  3. A model that has high bias
  4. A model that is too simple

Questions & Step-by-Step Solutions

What does the term 'overfitting' refer to in machine learning?
  • Step 1: Understand that in machine learning, we train models using data called 'training data'.
  • Step 2: Know that the goal of a model is to learn patterns from this training data.
  • Step 3: Realize that sometimes a model can learn the training data too well, including all the small details and noise.
  • Step 4: This excessive learning is called 'overfitting'.
  • Step 5: When a model is overfitted, it performs well on the training data but poorly on new, unseen data.
  • Step 6: The main issue with overfitting is that the model cannot generalize its knowledge to new situations.
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