What is the difference between overfitting and underfitting in machine learning?

What is the difference between overfitting and underfitting in machine learning?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. Underfitting happens when a model is too simple to capture the data's complexity.

Can you give me an example of overfitting?

Sure! Imagine a model that perfectly predicts the training data points but fails to generalize to new data. For instance, a polynomial regression model with a very high degree might fit all training points but perform poorly on unseen data.

How can I prevent overfitting?

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

What about underfitting? How can I avoid that?

To avoid underfitting, ensure your model is complex enough to capture the underlying patterns in the data. You can also try using more features or a more sophisticated algorithm.