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.

How can I tell if my model is overfitting?

You can check for overfitting by comparing the performance of your model on training data versus validation data. If the training accuracy is high but validation accuracy is low, your model may be overfitting.

What techniques can I use to prevent overfitting?

Common techniques include using cross-validation, pruning, regularization, and simplifying the model architecture.

Can you give an example of underfitting?

Sure! An example of underfitting is a linear regression model trying to fit a complex, non-linear dataset, resulting in poor performance on both training and validation data.