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.
If you've ever wondered how Spotify recommends songs you like or how Siri and Alexa can understand what you say to them... the answer is that you can [...]
Read More »Artificial intelligence is increasingly present in companies and its growth is being applied in practically all sectors. When the end [...]
Read More »The integration of tools for predictive analytics is already commonplace in large companies, but thanks to the evolution and, above all, to the dem [...]
Read More »Typically, Machine Learning is used to solve business problems in various sectors and areas where different algorithms are applied.
Read More »