Xinreality at 10:37, 23 January 2023

2023-01-23T10:37:30Z

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	If we have a dataset of n samples denoted by X = [x1, xn], and a probability distribution function (pdf] denoted P, then the i.i.d assumption is that:		If we have a dataset of n samples denoted by X = [x1, xn], and a probability distribution function (pdf] denoted P, then the i.i.d assumption is that:

	- Each sample xi comes from the distribution P.		*Each sample xi comes from the distribution P.
	- The samples are identically distributed. This means that they are drawn from the exact same distribution.		*The samples are identically distributed. This means that they are drawn from the exact same distribution.

	==Applications in Machine Learning==		==Applications in Machine Learning==

Xinreality: Created page with "==Introduction== In machine learning, independently distributed and identically distributed (i.i.d). This is a statistical assumption that data points in a dataset are indepen..."

2023-01-23T10:37:16Z

Created page with "==Introduction== In machine learning, independently distributed and identically distributed (i.i.d). This is a statistical assumption that data points in a dataset are indepen..."

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==Introduction==
In machine learning, independently distributed and identically distributed (i.i.d). This is a statistical assumption that data points in a dataset are independently drawn using the same probability distribution.

==Mathematical Definition==
If we have a dataset of n samples denoted by X = [x1, xn], and a probability distribution function (pdf] denoted P, then the i.i.d assumption is that:

- Each sample xi comes from the distribution P.
- The samples are identically distributed. This means that they are drawn from the exact same distribution.

==Applications in Machine Learning==
The i.i.d assumption can be used in supervised learning algorithms like linear regression and [[neural network]]. It can also be used in unsupervised learning algorithms like [[k-means Clustering]] or [[principal Component Analysis]].

Machine learning algorithms often use the i.i.d assumption because it allows them to use powerful mathematical tools like the [[central limit theory]] and the [[Law of Large Numbers]], which allow them to draw inferences about the underlying distribution.

==Explain Like I'm 5 (ELI5)==
In machine learning, I.i.d means that all data is identical and independent of each others. Each candy is identical and you can only eat one at a given time. This idea is used to help machine learning understand patterns in data.

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

Xinreality at 10:37, 23 January 2023

Xinreality: Created page with "==Introduction== In machine learning, independently distributed and identically distributed (i.i.d). This is a statistical assumption that data points in a dataset are indepen..."