Overfitting is a term used in statistics that refers to a modeling error that occurs when a function corresponds too closely to a particular set of data. As a result, overfitting may fail to fit additional data, and this may affect the accuracy of predicting future observations.

Low error rates and a high variance are good indicators of overfitting. In order to prevent this type of behavior, part of the training dataset is typically set aside as the “test set” to check for overfitting.

Overfitting can be identified by checking validation metrics such as accuracy and loss. The validation metrics usually increase until a point where they stagnate or start declining when the model is affected by overfitting. During an upward trend, the model seeks a good fit, which, when achieved, causes the trend to start declining or stagnate.

Below are some of the ways to prevent overfitting:

1. Training with more data
2. Data augmentation
3. Data simplification
4. Ensembling
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