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Understanding Overcomplete Features in Machine Learning

Overcomplete refers to a situation where a model or a set of features is too complex and captures more variation in the data than is necessary. In other words, the model or features are able to fit the noise in the data rather than the underlying patterns. This can lead to poor generalization performance on new data, as the model becomes overly specialized to the training data.

In the context of feature selection, overcomplete refers to a situation where there are more features than are needed to capture the important variations in the data. For example, if a model has 100 features but only 20 of them are truly relevant to the problem, then the other 80 features are considered overcomplete.

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