What are the signs of overfitting in machine learning models?

What are the signs of overfitting in machine learning models?

Overfitting occurs when a model learns the training data too well, including noise. Signs include high accuracy on training data but poor performance on validation data.

How can I identify if my model is overfitting?

You can identify overfitting by comparing the training and validation loss. If the training loss decreases while the validation loss increases, your model is likely overfitting.

What techniques can I use to prevent overfitting?

Common techniques include using cross-validation, regularization methods like L1 and L2, and simplifying the model architecture.

Is overfitting more common in certain types of models?

Yes, complex models like deep neural networks are more prone to overfitting, especially with limited training data.

Can overfitting be fixed after it occurs?

While you can't 'fix' overfitting directly, you can retrain the model with more data or apply regularization techniques to improve its generalization.