What are overfitting and underfitting in machine learning?

What are 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 examples of each?

Sure! An example of overfitting is a complex polynomial regression that fits every point in the training set but performs poorly on new data. An example of underfitting is a linear model trying to fit a quadratic relationship.

How can I prevent overfitting?

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

What about underfitting? How can I improve that?

To improve underfitting, you can increase the model complexity, add more features, or reduce regularization.