What are some examples of overfitting and underfitting in machine learning?

Can you explain what overfitting and underfitting mean in machine learning?

Sure! 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 underlying trend of the data.

Can you give me examples of each?

Absolutely! 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 regression model that fails to capture the curvature of the data.

How can I prevent overfitting?

You can prevent overfitting by using techniques like cross-validation, pruning in decision trees, or regularization methods like L1 and L2.

What about underfitting? How can I improve that?

To improve underfitting, you can try using a more complex model, adding more features, or reducing regularization.