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Attribute sampling: Difference between revisions
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
== | ==Introduction== | ||
Attribute sampling is a technique in machine learning to randomly select some features from a dataset to train a model. This process can be done for various reasons, such as saving computational time during training, avoiding overfitting risks, and increasing model interpretability. In this article we'll examine different types of attribute sampling, their advantages and drawbacks, and when they may be appropriate to use. | Attribute sampling is a technique in machine learning to randomly select some features from a dataset to train a model. This process can be done for various reasons, such as saving computational time during training, avoiding overfitting risks, and increasing model interpretability. In this article we'll examine different types of attribute sampling, their advantages and drawbacks, and when they may be appropriate to use. | ||
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[[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] [[Category:Not Edited]] | ||
{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
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