What are the main causes of overfitting in machine learning?

What are the main causes of overfitting in machine learning?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. Common causes include a complex model, insufficient training data, and excessive training.

Can you explain how a complex model leads to overfitting?

Certainly! A complex model has many parameters, allowing it to fit the training data very closely. However, this can lead to capturing noise and outliers, which do not generalize well to new data.

What can I do 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 there a way to identify if my model is overfitting?

Yes! You can monitor the training and validation loss. If the training loss decreases while the validation loss increases, your model is likely overfitting.