What causes overfitting in machine learning models?

What causes overfitting in machine learning models?

Overfitting occurs when a model learns the noise in the training data instead of the actual patterns. This can happen due to a model being too complex, having too many parameters, or when there is insufficient training data.

Can you give me an example of overfitting?

Sure! Imagine a model trained to recognize cats in images. If it memorizes the specific cats in the training set instead of learning general features, it may fail to recognize new cats.

How can I prevent overfitting?

You can prevent overfitting by using techniques like cross-validation, regularization, pruning, and ensuring you have enough training data.

What is regularization?

Regularization is a technique used to reduce overfitting by adding a penalty for larger coefficients in the model, which encourages simpler models.