How can I identify overfitting in my machine learning model?

How can I identify overfitting in my machine learning model?

Overfitting occurs when your model learns the training data too well, including noise. You can identify it by comparing the performance of your model on training and validation datasets.

What are some signs that my model is overfitting?

Common signs include a high accuracy on the training set but significantly lower accuracy on the validation set, and a model that performs poorly on unseen data.

How can I prevent overfitting?

You can prevent overfitting by using techniques like cross-validation, regularization, pruning, and by simplifying your model.

What is cross-validation?

Cross-validation is a technique used to assess how the results of a statistical analysis will generalize to an independent dataset. It helps in ensuring that your model is not overfitting.