Independently and identically distributed (i.i.d.): Difference between revisions

Created page with "{{see also|Machine learning terms}} ==Introduction== Machine learning algorithms often make the assumption of independently and identically distributed (i.i.d.) data, which implies each data point is drawn independently from a given probability distribution. This assumption is essential for many machine learning algorithms as it permits powerful mathematical operations to make predictions based on observed patterns in the data. ==Definition of i.i.d. data== Formally spe..."
(Created page with "{{see also|Machine learning terms}} ==Introduction== Machine learning algorithms often make the assumption of independently and identically distributed (i.i.d.) data, which implies each data point is drawn independently from a given probability distribution. This assumption is essential for many machine learning algorithms as it permits powerful mathematical operations to make predictions based on observed patterns in the data. ==Definition of i.i.d. data== Formally spe...")
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