View source for Independently distributed and identically distributed (i.i.d)

==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.