CHAID is a classification trees program developed by Kass (1980) that performs multi-level splits when computing classification trees. For discussion of the differences of CHAID from other classificat [..]

If you split the distribution in half at its mean (or median), the distribution of values on the two sides of this central point would not be the same (i.e., not symmetrical) and the distribution woul [..]

A distribution that has multiple modes (thus two or more "peaks").

Back propagation is the best known training algorithm for neural networks and still one of the most useful. Devised independently by Rumelhart et. al. (1986), Werbos (1974), and Parker (1985), it is t [..]

Basis functions of predictor variables (X) play an important role in the estimation of Multivariate Adaptive Regression Splines (MARSplines). Specifically, MARSplines uses two-sided truncated function [..]

When a model involving q parameters is fitted to data with n observations, the Bayesian Information criterion is defined as - 2 Lq + q * ln(n), where Lq is the maximized log-likelihood. This goodness [..]

Networks based on Bayes' theorem, on the inference of probability distributions from data sets. See also, probabilistic and generalized regression neural networks.

Bayesian analysis is an approach to statistical analysis that is based on the Bayes's law, which states that the posterior probability of a parameter p is proportional to the prior probability [..]

The Bernoulli distribution best describes all situations where a "trial" is made resulting in either "success" or "failure," such as when tossing [..]

The Beta coefficients are the regression coefficients you would have obtained had you first standardized all of your variables to a mean of 0 and a standard deviation of 1. Thus, the advantage of Beta [..]