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Artificial Intelligence & Machine Learning vocabulary · Model Training

Underfitting

Meaning in artificial intelligence & machine learning, with examples

/ˌʌndərˈfɪtɪŋ/nounCommon term

In artificial intelligence & machine learning, underfitting means: A 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.

In Turkish: yetersiz öğrenme, eksik uyum

How it is used

The opposite of overfitting. The usual sign is high training loss and high validation loss together. Typical fixes are a bigger model, better features, longer training or less regularization. The verb is underfit.

Example sentences

  1. Both losses are still high, so this looks like underfitting, not overfitting.

  2. A linear model leads to underfitting on data this complex.

Common collocations

  • signs of underfitting
  • fix underfitting
  • underfit the data
  • overfittingA 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.
  • 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.
  • modelThe result of training: a program with learned parameters that takes an input, such as an image or a sentence, and produces an output, such as a label, a number or a text.
  • loss functionA formula that measures how far a model's predictions are from the correct answers. Training tries to make this value, called the loss, as small as possible.

This word in other fields

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

More terms: Model Training

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