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

₹0.0

Question: What does the term \'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 to unseen data.

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

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.

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