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 explain how a complex model leads to overfitting?

Certainly! A complex model, like a deep neural network, has many parameters that can fit the training data very closely. If the model is too flexible, it may capture noise and outliers, leading to poor generalization on new data.

What are some ways to prevent overfitting?

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

Is overfitting a common problem in all types of machine learning?

Yes, overfitting can occur in any type of machine learning, but it's particularly common in complex models and when the training dataset is small.