Definition: Overfitting occurs when a machine learning model becomes too complex and learns the training data too well, leading to poor performance on new, unseen data.

Detailed Description:

Overfitting can occur when a model is too complex relative to the amount of training data, or when the model is not regularized to prevent overfitting. This can result in the model memorizing the training data rather than learning general patterns.

To avoid overfitting, techniques like regularization, cross-validation, and early stopping can be used.

Related Terms:

• Machine Learning
• Model Evaluation
• Underfitting
• Regularization
• Cross-Validation

FAQs:

1. What are the signs of overfitting?

   • High performance on the training set but poor performance on the validation set.

2. How can overfitting be prevented?

   • Techniques like regularization, early stopping, and increasing the amount of training data can help prevent overfitting.

3. What is the difference between overfitting and underfitting?

   • Overfitting occurs when a model is too complex and learns the training data too well, while underfitting occurs when a model is too simple and cannot learn the underlying patterns in the data.