Prediction Models in Machine Learning by Classification and Regression

Authors

  • Shamy Singh Research Scholar, NoorulIslam University, Tamil Nadu, India
  • J. Dheeba Associate Professor, CSE, NoorulIslam University, Tamil Nadu, India

DOI:

https://doi.org/10.53555/nncse.v3i5.419

Keywords:

Classification and regression tree algorithm, cross-validation, discriminant, linear model, prediction accuracy, recursive partitioning, selection bias, unbiased, QUEST, GUIDE, CRUISE, C4.5, RPART

Abstract

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 ofunordered 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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Published

2016-05-31

How to Cite

Singh, S., & Dheeba, J. (2016). Prediction Models in Machine Learning by Classification and Regression. Journal of Advance Research in Computer Science & Engineering (ISSN 2456-3552), 3(5), 10-14. https://doi.org/10.53555/nncse.v3i5.419