Underfitting
Underfitting is a common problem in machine learning where a model is too simple to capture the underlying patterns in the data. This occurs when the model is not complex enough to fit the training data, resulting in poor performance on both the training and test data. Underfitting can be caused by using a model that is too simple, using too few features, or not training the model for long enough. Underfitting can be detected by comparing the performance of the model on the training and test data. If the model performs poorly on both, it is likely underfitting. To address underfitting, one can use a more complex model, add more features, or train the model for longer. However, it is important to avoid overfitting, where the model becomes too complex and fits the noise in the data, rather than the underlying patterns.
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