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Feature set: Difference between revisions
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
In machine learning, a [[feature set]] refers to the collection of [[input]] variables or [[feature]]s that the [[machine learning model]] trains on. These variables are selected based on their relevance to the problem being solved and their capacity for making accurate predictions. | In [[machine learning]], a [[feature set]] refers to the collection of [[input]] variables or [[feature]]s that the [[machine learning model]] trains on. These variables are selected based on their relevance to the problem being solved and their capacity for making accurate predictions. | ||
The feature set is an essential component of any machine learning model, as its quality and relevance directly influence its performance. For instance, if there are too many irrelevant or noisy elements present, then predictions may not be as accurate. | The feature set is an essential component of any machine learning model, as its quality and relevance directly influence its performance. For instance, if there are too many irrelevant or noisy elements present, then predictions may not be as accurate. | ||