What are some examples of overfitting in machine learning?

What are some examples of overfitting in machine learning?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. For example, if a model is trained on a small dataset of images of cats and dogs, it might memorize the specific images rather than generalizing to new images.

Can you give me another example?

Sure! Imagine a model predicting house prices based on a limited dataset. If it learns to associate specific features of the training houses too closely, it may fail to predict prices accurately for new houses that have different characteristics.

How can we prevent overfitting?

To prevent overfitting, you can use techniques like cross-validation, regularization, and pruning. Additionally, using more training data can help the model generalize better.

What is regularization?

Regularization is a technique used to reduce overfitting by adding a penalty to the loss function for large coefficients in the model. This encourages the model to keep the weights small and simplifies the model.