Overfitting and underfitting are common issues that arise during the training of machine learning models. Both problems affect the model's ability to generalize to new, unseen data, and they can have adverse effects on the model's performance and reliability.
Overfitting occurs when a machine learning model performs very well on the training data but poorly on new, unseen data. In other words, the model "memorizes" the training data rather than learning general patterns and relationships. As a result, the model becomes too specific to the training data's noise and outliers, leading to poor performance on test or validation data.
Signs of overfitting:
- Low training error (model performs well on training data).
- High test or validation error (model performs poorly on new data).
- The model captures noise or outliers in the training data.
Causes of overfitting:
- Too complex model: Models with a large number of parameters or high model complexity are more prone to overfitting, as they have the capacity to fit the noise in the training data.
- Insufficient data: If the training dataset is too small, the model may learn the training data too well, including noise and outliers.
How to address overfitting:
- Use regularization techniques like L1 or L2 regularization to penalize large model weights.
- Reduce model complexity or use simpler models.
- Increase the amount of training data.
Finding the right balance between model complexity and generalization is crucial to avoid both overfitting and underfitting. Regularization, cross-validation, and proper dataset splitting are common techniques used to diagnose and mitigate these issues.