Open AccessPrediction Models in Machine Learning by Classification and Regression
Author(s) -
Shamy Singh,
J. Dheeba
Publication year - 2016
Publication title -
journal of advance research in computer science and enigneering
Language(s) - English
Resource type - Journals
ISSN - 2456-3552
DOI - 10.53555/nncse.v3i5.419
Subject(s) - decision tree , regression , machine learning , regression analysis , computer science , artificial intelligence , tree (set theory) , decision tree learning , linear regression , simple (philosophy) , predictive modelling , data mining , statistics , mathematics , mathematical analysis , philosophy , epistemology
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.