Contents
What is random forest in layman terms?
Random Forest Classifier is an ensemble algorithm, which creates a set of decision trees from a randomly selected subset of the training set, which then aggregates the votes from different decision trees to decide the final class of the test object.
Where do we use decision tree?
Decision trees are used for handling non-linear data sets effectively. The decision tree tool is used in real life in many areas, such as engineering, civil planning, law, and business. Decision trees can be divided into two types; categorical variable and continuous variable decision trees.
Which is the best description of a decision tree?
A decision tree is a graphical representation of all the possible solutions to a decision based on certain conditions. Tree models where the target variable can take a finite set of values are called classification trees and target variable can take continuous values (numbers) are called regression trees.
How are decision trees used in machine learning?
Decision trees belong to a class of supervised machine learning algorithms, which are used in both classification (predicts discrete outcome) and regression (predicts continuous numeric outcomes) predictive modeling. The goal of the algorithm is to predict a target variable from a set of input variables and their attributes.
How to explain decision trees and random forests?
Decision trees look at the primary features that may give us insight on a response, and then splits it. Let’s say you want to predict whether a patient entering an ER is high risk or not. A decision tree may show us that we should first consider whether they are over 50 years old (the first split, one side for over, another for under).
How is variance reduction used in decision trees?
Introduced in the CART algorithm, decision tree regressors use variance reduction as a measure of the best split. Variance reduction indicates how homogenous our nodes are. If a node is extremely homogeneous, its variance (and the variance of its child nodes) will not be as big.