What are the effects of overfitting and underfitting in machine learning?

What are the effects of overfitting and underfitting in machine learning?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. This leads to poor performance on new data. Underfitting, on the other hand, happens when a model is too simple to capture the underlying trend, resulting in low accuracy on both training and test data.

How can I identify if my model is overfitting or underfitting?

You can identify overfitting by comparing the training and validation accuracy. If the training accuracy is high but validation accuracy is low, it's likely overfitting. For underfitting, both training and validation accuracies will be low.

What techniques can I use to prevent overfitting?

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

Can you explain regularization in more detail?

Regularization adds a penalty to the loss function to discourage overly complex models. Common methods include L1 (Lasso) and L2 (Ridge) regularization, which help to keep the model weights small and reduce overfitting.