It builds classification models in the form of a tree structure that represents rules that can be easily understood.

This predicts the class of previously unseen records by aggregating predictions made by multiple classifiers.

 

A relatively modern algorithm that is essentially an ensemble of decision trees.

Assumes that there is a linear relationship present between dependent and independent variables. In simple words, it finds the best fitting line/plane that describes two or more variables.

As a statistical approach, it is used to predict the relationship between two variables the dependent variable, Y, and the independent variable, X.

A numerical variable used in regression analysis to represent subgroups of the sample in your study.

A technique used to measure the degree to which the various independent variable and various dependent variables are linearly related to each other.

The size of the weight indicates the precision of the information contained in the associated observation.

The linear model assumes that the conditional expectation of the dependent variable Y is equal to a linear combination of the explanatory variables X.

Can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance.