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	Podcast:		Linear Digressions
	Episode:		Regularization
	Category:		Technology
	Duration:		00:17:27
	Publish Date:		2016-10-02 21:13:50
	Description:		Lots of data is usually seen as a good thing. And it is a good thing--except when it's not. In a lot of fields, a problem arises when you have many, many features, especially if there's a somewhat smaller number of cases to learn from; supervised machine learning algorithms break, or learn spurious or un-interpretable patterns. What to do? Regularization can be one of your best friends here--it's a method that penalizes overly complex models, which keeps the dimensionality of your model under control.
	Total Play:		0

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