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Independently and identically distributed (i.i.d.): Difference between revisions
Independently and identically distributed (i.i.d.) (view source)
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
[[Machine learning]] [[algorithm]]s 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. | [[Machine learning]] [[algorithm]]s 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== | ==Definition of i.i.d. data== | ||