Explain RANDOM FOREST: An algorithm used for regression or classification that uses a collection of tree data structures. “To classify a new object from an input vector, put the input vector down each of the trees in the forest. Each tree gives a classification, and we say the tree ‘votes’ for that class. The forest chooses the classification having the most votes (over all the trees in the forest).”[breiman] The term “random forest” is actually trademarked by its authors. See also classification, vector, decision trees.
Different definitions in web development like random forest in Dictionary R.
- Manual Ruby:
- Meaning language that first appeared in 1996. Ruby is popular in the data science community, but not as popular as Python, which has more specialized libraries available for data science tasks. See also random forest definition.
- Manual Root Mean Squared Error:
- Meaning square root of the Mean Squared Error. This is more popular than Mean Squared Error because taking the square root of a figure built from the squares of the observation value errors gives a number random forest explain.
- Manual Regression:
- Meaning general problem of fitting any kind of model to any kind of data. This use of the term 'regression' is a historical accident; it is only indirectly related to the original meaning of the random forest what is.
- Manual R:
- Meaning An open-source programming language and environment for statistical computing and graph generation available for Linux, Windows, and Mac random forest meaning.
- Manual Reinforcement Learning:
- Meaning learning algorithms in which the process is not given specific goals to meet but, as it makes decisions, is instead given indications of whether it’s doing well or not. For example, an algorithm for random forest abbreviation.
