What is overfitting in machine learning?

Overfitting in machine learning occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the model’s performance on new data. This means the model has learned the training data too well, capturing noise and patterns that do not generalize to unseen data. Overfitting leads to a model that has high accuracy on its training data but performs poorly on any unseen data, essentially because it has memorized the training data rather than learned to generalize from it.

Overfitting is a common problem in machine learning, especially in models that are too complex for the amount of training data available. It can be detected by a significant difference in accuracy between the training and validation datasets. To combat overfitting, techniques such as cross-validation, pruning, regularization, and reducing the complexity of the model can be employed. Furthermore, increasing the size of the training data can also help reduce the risk of overfitting by providing the model with more examples from which to learn generalizable patterns.