assignment5 - of regression models 1 Linear regression...

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Data Mining Assignment #5 CSC592 – Fall ‘05 Problem Statement You are given a data set on CPU performance (cpu.arff), where the following are the independent variables: MYCT: machine cycle time in nanoseconds (integer) MMIN: minimum main memory in kilobytes (integer) MMAX: maximum main memory in kilobytes (integer) CACH: cache memory in kilobytes (integer) CHMIN: minimum channels in units (integer) CHMAX: maximum channels in units (integer) And PERF is a performance index and is considered the dependent variable: PERF: published performance index (integer) The goal is to build regression models that fit this data. In particular you are to build three types
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Unformatted text preview: of regression models: 1) Linear regression models 2) Regression trees (NOT model trees) 3) Neural networks In each category you are to build the best possible model using the root mean squared (RMS) error as your evaluation criterion. Use 10-fold cross-validation to compute the error. Make sure you explain your choice of model parameters and the tradeoffs you have made. Which one of the models is your best model overall? Handing in your assignment Write a description of your experiments and your findings and submit this together with your models. The due date is Friday, November 21 st in class....
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