Overfitting

Overfitting occurs when a machine learning model learns the training data too well, capturing noise and details that do not generalize to new data.

Overfitting is a common problem in machine learning, leading to models that perform well on training data but poorly on unseen data. It typically happens when the model is too complex relative to the amount of training data, such as having too many parameters or using a very flexible model.

An illustrative example of overfitting is a polynomial regression model that fits a high-degree polynomial to a small dataset. While the model may fit the training data perfectly, it will likely perform poorly on new data due to its sensitivity to small fluctuations in the training data. Another example is a decision tree, that ist prone to overfit its internal decisions if only few datapoints are used.

To detect and prevent overfitting, techniques such as cross-validation can be used. Cross-validation involves partitioning the data into subsets, training the model on some subsets, and validating it on the remaining subsets. This helps to ensure that the model generalizes well to new data.

Other techniques to prevent overfitting include regularization, pruning, and using simpler models. Regularization adds a penalty to the loss function to discourage overly complex models. Pruning reduces the complexity of decision trees by removing branches that have little importance. Using simpler models with fewer parameters can also help to reduce the risk of overfitting.

Overfitting is an essential concept in machine learning to understand and address, ensuring that models perform well on both training and unseen data.