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Software Engineering vocabulary · Machine Learning and Data Processing

Overfitting

Meaning in software engineering, with examples

/ˌoʊvərˈfɪtɪŋ/nounCore term

In software engineering, overfitting means: A problem in which a model learns its training data too closely, including noise and accidents, so it scores well on that data but poorly on new data.

In Turkish: aşırı öğrenme, aşırı uyum, ezberleme

How it is used

A classic warning sign is training loss going down while validation loss goes up. The verb is overfit: a model overfits, or overfits to the training set. Engineers fight it with more data, regularization, dropout and early stopping.

Example sentences

  1. Validation loss started rising after epoch 8, a clear sign of overfitting.

  2. With only 300 examples, overfitting is almost guaranteed.

Common collocations

  • reduce overfitting
  • prevent overfitting
  • signs of overfitting
  • overfit to the training data
  • underfittingA problem in which a model is too simple or trained too little to capture the patterns in the data, so it performs poorly even on the training data.
  • regularizationAny technique that stops a model from becoming too complex during training, in order to reduce overfitting and help it work better on new data.
  • validation setA part of the data kept out of training and used during development to compare models, tune settings and decide when to stop training.
  • training dataThe examples a model learns from during training. Their quality, size and variety largely decide how well the model works on new data.

This word in other fields

The same English word can have a different meaning in another field:

More terms: Machine Learning and Data Processing

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