Random Forest [Tree]

Description

The random forest is a classification algorithm consisting of many decision trees. It uses bagging and features randomness when building each tree to try to create an uncorrelated forest of trees whose prediction by committee is more accurate than that of any individual tree.

One of the most commonly known ensemble models is random forest, where the model combines the predictions of multiple decision trees and outputs the predictions. This is usually more accurate and prone to overfitting.

تو این الگوریتم با انتخاب تصادفی از دیتا ست اصلی چندین دیتا ست کوچیک تر میسازه و از رو هر کدوم یک decision tree میسازه و ورودی رو به همه اون ها میده و در نهایت بین جواب های حاصل برایند میگیره و نظر نهاییش رو اعلام میکنه

Vs PCA

• In each of the supervised learning use cases, random forest can be used to reduce the number of dimensions in data.
• For unsupervised dimensionality reduction tasks, PCA can be helpful.