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What does overfitting refer to in supervised learning?
What does overfitting refer to in supervised learning?
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What does overfitting refer to in supervised learning?
The model performs well on unseen data
The model is too simple to capture the data patterns
The model learns noise in the training data
The model has high bias
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Overfitting occurs when a model learns noise in the training data, leading to poor performance on unseen data.
Questions & Step-by-step Solutions
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Q
Q: What does overfitting refer to in supervised learning?
Solution:
Overfitting occurs when a model learns noise in the training data, leading to poor performance on unseen data.
Steps: 5
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Step 1: Understand that supervised learning involves training a model on a set of data with known outcomes.
Step 2: Recognize that the model tries to learn patterns from this training data.
Step 3: Realize that sometimes the model can learn not just the important patterns, but also random noise or irrelevant details in the training data.
Step 4: This excessive learning of noise is called overfitting.
Step 5: When a model is overfitted, it performs very well on the training data but poorly on new, unseen data.
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