Underfitting is a term used in machine learning that refers to a model that cannot capture the complexity of the training data and therefore does not adequately fit it. In other words, the model is too simple and is not able to capture the underlying relationships between the input data and the output labels.
When a model under-fits, it is likely to have a high bias, meaning that it is oversimplified and cannot adequately model the complexity of the input data. The result is a model that performs poorly on training data as well as on test or validation data.
Underfitting can occur due to several reasons, such as the selection of an inappropriate model, the use of irrelevant features, the lack of sufficient training data, the use of an insufficient training process, among others.
To solve the problem of underfitting, it is possible to use techniques such as data augmentation, selection of relevant features, selection of more complex models, regularisation, and hyperparameter optimisation. These techniques help to improve model performance and avoid underfitting the training data.
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