In the context of neural networks, what is 'overfitting'?

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Question: In the context of neural networks, what is \'overfitting\'?

Options:

Correct Answer: When the model performs well on training data but poorly on unseen data

Solution:

Overfitting occurs when a model learns the training data too well, including noise, leading to poor generalization on new data.

In the context of neural networks, what is 'overfitting'?

In the context of neural networks, what is 'overfitting'?

Step 1: Understand that a neural network is a type of model used to learn from data.
Step 2: When we train a neural network, we give it a set of training data to learn from.
Step 3: Overfitting happens when the model learns the training data too well, including all the small details and noise.
Step 4: Because of this, the model becomes very good at predicting the training data but struggles to make accurate predictions on new, unseen data.
Step 5: In simple terms, overfitting means the model is too specialized to the training data and cannot generalize well.

Overfitting – Overfitting occurs when a model learns the training data too well, including noise, leading to poor generalization on new data.