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Activity Number: 432
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #320748
Title: Statistical Learning Toolbox for Prediction
Author(s): Umashanger Thayasivam*
Companies: Rowan University
Keywords: Learning ; Supervised ; Unsupervised ; prediction ; empirical ; cross-validation

The application of statistical methods to very large data sets with many variables has experienced dramatic growth over the past few years. Data mining is a broad subject that encompasses several topics and problems including supervised and unsupervised learning. In supervised learning problems of classification and regression, these concerns are effectively addressed by cross-validation-that is, by dividing the data into a training subset to build a prediction model and a test subset to evaluate the model's performance. Empirical comparison of Classification techniques including naive Bayes, support vector machine, decision tree, and random forest were studied and a Classification Learning Toolbox was developed using R statistical programming language to analyze the date sets and report the relationships and prediction accuracy between the classifiers.

Authors who are presenting talks have a * after their name.

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