Term
Overfitting
Definition
Overfitting happens when a model learns the details and noise of the training data to an extent that it negatively impacts its performance on new, unseen data. This occurs when a model is too focused on one set of data and struggles to apply what it has learned to different situations.
Where you’ll find it
In AI platforms, overfitting is primarily discussed in the context of training machine learning models. You might encounter discussions or settings related to overfitting in tools or panels where model training settings are configured, such as in data preprocessing or model evaluation sections.
Common use cases
- Optimizing machine learning models to ensure they perform well on both the training data and new, unseen datasets.
- Adjusting the complexity of a model during the training process to prevent overfitting.
- Utilizing techniques like cross-validation during model training to monitor for overfitting.
Things to watch out for
- Overfitting can make a model perform exceptionally well on training data but poorly on anything different, affecting its practical usefulness.
- High accuracy on training data does not mean the model is good; the true test is its performance on new data.
- Avoid using overly complex models for simple problems, as they are more prone to overfitting.
Related terms
- Generalization
- Training Set
- Cross-validation
- Regularization
- Model Complexity