What is the difference between overfitting and underfitting in AI training?

What is the difference between overfitting and underfitting in AI training?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. Underfitting happens when a model is too simple to capture the data's complexity.

Can you give me an example of overfitting?

Sure! Imagine a model trained to recognize cats. If it memorizes every cat in the training set, it might fail to recognize new cats that look different, which is overfitting.

And what about underfitting?

Underfitting would be like using a linear model to predict a complex, non-linear relationship. The model wouldn't capture the trends in the data, leading to poor performance.

How can I prevent overfitting?

You can prevent overfitting by using techniques like cross-validation, regularization, and pruning, or by simplifying your model.

What are some signs that my model is overfitting?

Signs of overfitting include a high accuracy on the training set but significantly lower accuracy on the validation or test set.