Independently and identically distributed (i.i.d.): Revision history

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17 March 2023

24 February 2023

  • curprev 19:0619:06, 24 February 2023‎ Alpha5 talk contribs‎ 3,527 bytes +4‎ No edit summary
  • curprev 19:0519:05, 24 February 2023‎ Alpha5 talk contribs‎ 3,523 bytes −480‎ No edit summary
  • curprev 18:3618:36, 24 February 2023‎ Alpha5 talk contribs‎ 4,003 bytes +4,003‎ 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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