What is random forest Regressor?
Also to know is, how does a random forest Regressor work?
In other words, Random forest builds multiple decision trees and merge their predictions together to get a more accurate and stable prediction rather than relying on individual decision trees. Each tree in a random forest learns from a random sample of the training observations.
Also Know, is Random Forest a regression model? Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual
Keeping this in consideration, what is random in random forest?
The random forest is a classification algorithm consisting of many decisions trees. It uses bagging and feature randomness when building each individual tree to try to create an uncorrelated forest of trees whose prediction by committee is more accurate than that of any individual tree.
What is random forest in ML?
In machine learning, the random forest algorithm is also known as the random forest classifier. It is a very popular classification algorithm. So basically, what a random forest algorithm does is that it creates multiple decision trees and merges them together to obtain a more stable and accurate prediction.