Prediction Models in Machine Learning by Classification and Regression
DOI:
https://doi.org/10.53555/nncse.v2i5.408Keywords:
Classification and regression tree algorithm, cross-validation, discriminant, linear model, prediction accuracy, recursive partitioning, selection bias, unbiased, QUEST, GUID, CRUISE, C4.5, RPARTAbstract
One of the machine-learning method for constructing prediction models from data is Classification and Regression. By partitioning the data space recursively these models are configuring and in each prediction model are fitting with a simple predictions. Finally, the partitioning can be represented pictorially as a decision tree. Finite number of unordered values are taken for the designing the classification trees and are designed for independent variables. And the prediction error are measured in terms of misclassification cost. Squared difference between the predicted and observed values are measured in regression trees, which are dependent variables that have ordered discrete values or continuous values. Here in this article reviewing and comparing some of the widely acceptable algorithms such as QUEST, GUIDE, CRUISE, C4.5 and RPART with their strengths, weakness and capabilities.
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