What does overfitting refer to in supervised learning?

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Question: What does overfitting refer to in supervised learning?

Options:

Correct Answer: The model learns noise in the training data

Solution:

Overfitting occurs when a model learns noise in the training data, leading to poor performance on unseen data.

What does overfitting refer to in supervised learning?

What does overfitting refer to in supervised learning?

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.

Overfitting – Overfitting refers to a model that learns the details and noise in the training data to the extent that it negatively impacts its performance on new data.