What does overfitting refer to in machine learning?

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Question: What does overfitting refer to in machine learning?

Options:

Correct Answer: A model that 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.

What does overfitting refer to in machine learning?

What does overfitting refer to in machine learning?

Step 1: Understand that in machine learning, we train models using data called 'training data'.
Step 2: A model is supposed to learn patterns from this training data to make predictions.
Step 3: Overfitting happens when the model learns the training data too well, including its mistakes and random noise.
Step 4: When a model is overfitted, it performs very well on the training data but poorly on new, unseen data.
Step 5: This means the model cannot generalize its learning to make accurate predictions outside of the training data.

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