Overfitting in machine learning

by: ExpertAI

Overfitting occurs when a machine learning model becomes too complex and starts to fit the training data too closely, resulting in poor generalization performance on new, unseen data. This happens because the model has essentially memorized the training data instead of learning the underlying patterns and relationships.

One common cause of overfitting is having too few training examples relative to the complexity of the model. In such cases, the model may be able to fit the training data perfectly, but it will not be able to generalize to new data.

To avoid overfitting, it is important to use techniques such as regularization, early stopping, and dropout. Regularization adds a penalty term to the loss function to discourage the model from learning complex or irrelevant features. Early stopping stops the training process before the model becomes too complex and starts to overfit. Dropout randomly drops out some of the neurons in the model during training, which helps to prevent overfitting by forcing the model to learn more robust features.

Overall, overfitting is an important consideration in machine learning, and it is crucial to take proactive steps to prevent it in order to ensure that our models are able to generalize well to new, unseen data.