![]() Contrary to the global parametric models, MARS operates locally. Instead, this relationship is determined based on regression data. In this method, no assumptions about the analyzed functional relationship between variables are made. ![]() ![]() Moreover, MARS makes it possible to fit non-linear multivariate functions. It is “a nonparametric regression method that approximates a complex non-linear relationship with a series of spline functions defined on different intervals of the independent (predictor) variable”. MARS, introduced by Jerome Friedman in 1991, is mainly used for solving regression-type problems. These methods are more and more frequently applied to various issues associated with animal breeding and husbandry.Ģ.1. ![]() Among many different methods belonging to data mining, the following can be distinguished: the general models of classification and regression trees (G_Trees), general CHAID (Chi-square Automatic Interaction Detection) models, interactive classification and regression trees (also with boosting – Boosted Trees), random forest, MARS (Multivariate Adaptive Regression Splines), artificial neural networks (ANN), other machine learning methods such as: naïve Bayes classifier (NBC), support vector machines (SVM), k-nearest neighbors (k-NN) and other regarded (or not) by different authors as data mining techniques. The characteristic feature of these models is the use of a specific strategy, usually requiring the division of data into training set, sometimes also verification set, which enable the evaluation of the model quality as well as a test set for checking its prognostic or classification abilities. However, they can also be used in a much narrower range, sometimes as an alternative to classical statistics. Data mining techniques involve mainly searching for various relationships in large data sets. ![]()
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