In supervised learning, what does overfitting refer to?

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Question: In supervised learning, what does overfitting refer to?

Options:

Correct Answer: Model performs well on training data but poorly on unseen data

Solution:

Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to new data.

In supervised learning, what does overfitting refer to?

In supervised learning, what does overfitting refer to?

Step 1: Understand that supervised learning involves training a model on a set of data with known outcomes.
Step 2: Realize that the goal of the model is to learn patterns from this training data.
Step 3: Know that overfitting happens when the model learns the training data too well, including its noise and outliers.
Step 4: Recognize that a model that is overfitted performs very well on the training data but poorly on new, unseen data.
Step 5: Conclude that overfitting means the model cannot generalize its learning to other data sets.

Overfitting – Overfitting refers to a model's excessive learning of the training data, including its noise, which leads to poor performance on unseen data.