A sample of data used to validate the model

Variable discretization is a process of converting a variable with continuous scale (interval variable) into discrete categories (a categorical variable).

A measure of variable importance, indicating the relative contribution of each input variable in making predictions. It allows users to identify the most influential predictors and gain insights into the underlying data patterns.

A characteristic that describes the nature and properties of a variable; it indicates the type of values that a variable can take and the operations that can be performed on it

Variance of a variable measures how much the values of that variable vary from their mean. In PCA, the goal is to find the directions in the data with the highest variance, as these directions contain the most information about the data.

Varimax is a orthogonal rotation method that. aims to maximize the variance of the squared loadings on each component, leading to a simple and sparse structure where each variable has a high loading on one component and close to zero loadings on other components.

Voluntary response sampling is a non-probabilistic sampling method where participants self-select to be part of the sample, often in response to a survey or study invitation, which can lead to biased results due to the voluntary nature of participation.

During prediction, each decision tree independently generates a prediction, and the final prediction of the random forest is determined through voting (for classification models) or averaging (for association models) the individual tree predictions. This ensemble approach helps reduce bias and variance in the modeling and improves the overall accuracy and robustness of the model.