Title page for ETD etd-11012006-192748


Type of Document Master's Thesis
Author Lahiri, Rochana
Author's Email Address rlahir1@lsu.edu
URN etd-11012006-192748
Title Comparison of Data Mining and Statistical Techniques for Classification Model
Degree Master of Science (M.S.)
Department Information Systems & Decision Sciences (Business Administration)
Advisory Committee
Advisor Name Title
Helmut S. Schneider Committee Chair
Edward F. Watson Committee Member
Young H. Chun Committee Member
Keywords
  • sample size
  • classification
  • data mining
Date of Defense 2006-10-30
Availability unrestricted
Abstract
The purpose of this study is to observe the performance of three statistical and data mining classification models viz., logistic regression, decision tree and neural network models for different sample sizes and sampling methods on three sets of data. It is a 3 by 2 by 3 by 8 study where each statistical or data mining method has been employed to build a model for each of 8 different sample sizes and two different sampling methods. The effect of sample size on the overall performance of each model against two sets of test data are observed and compared.

It is seen that for a given dataset, none of the three methods is found to outperform any other and their performances are comparable. This is in contrast to many of the existing studies as cited in the literature review chapter of this thesis. But the absolute value of prediction accuracy varied between the three datasets indicating that the data distribution and data characteristics play a role in the actual prediction accuracy, especially the ratio of the binary values of the dependent variable in the training dataset and the population. The models built with each of the sample size and sampling method for each method were run on two sets of test data to test whether the prediction accuracy was being replicated. It was found that for each of the cases the prediction accuracy was replicated across the test datasets.

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