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Computer systems performance evaluation project

Small Project 1: Linear Reg In this project, you will get to use WEKA Tool . Please soethe References and Resources Section for guidelines on how to get this tool using Linear Regression, implemented in WEKA. Your task is to build models to predict the number of faults based on the other e This assignment involves building and evaluating fault prediction models attributes of programs in the dataset. Each model is to be built and evaluated using 10-fold cross validation on the fit data set, and then validated using the test data set The datasets have already been preprocessed for use in Weka. You could download the datasets from the link under Reflerences& Resources. the model, you will get several statistical indicators, the measures of the quality of fit (in the case of fit data) and the Use the fit dataset to build models based on 10-fold cross validation, when you predictive quality (for the test data), at the end of cach run, as listed below: Correlation coeficient Mean absolute error (also called AAE, which stands for Average Absolute Error) Relative absolute erroe Root relative squared error Attr hate Selection MS method The Linear regression models could be built with three different options for actnbute selection in wEKA Greedy method Ys ha e use cach attribute el tion method for building the models. Consequently, you win have three different models. Com are the models, how many and which independent van ies were selecte Afer building the models, evaluate their performance by supplying the test data set. Compare the quality of fit and predictive quality for each model built. Also compare the qualities of fit and predictive qalities amcing all the differet models respectively. Your comparisons should not be basod on just one parameter. Use all the satistical indicators (mentioned hereabove) provided by Weka to perform the comparisons Dont forget to include all the results based on the 10-fold cross validation andthe lest data set for each model. Small Project 1: Linear Reg In this project, you will get to use WEKA Tool . Please soethe References and Resources Section for guidelines on how to get this tool using Linear Regression, implemented in WEKA. Your task is to build models to predict the number of faults based on the other e This assignment involves building and evaluating fault prediction models attributes of programs in the dataset. Each model is to be built and evaluated using 10-fold cross validation on the fit data set, and then validated using the test data set The datasets have already been preprocessed for use in Weka. You could download the datasets from the link under Reflerences& Resources. the model, you will get several statistical indicators, the measures of the quality of fit (in the case of fit data) and the Use the fit dataset to build models based on 10-fold cross validation, when you predictive quality (for the test data), at the end of cach run, as listed below: Correlation coeficient Mean absolute error (also called AAE, which stands for Average Absolute Error) Relative absolute erroe Root relative squared error Attr hate Selection MS method The Linear regression models could be built with three different options for actnbute selection in wEKA Greedy method Ys ha e use cach attribute el tion method for building the models. Consequently, you win have three different models. Com are the models, how many and which independent van ies were selecte Afer building the models, evaluate their performance by supplying the test data set. Compare the quality of fit and predictive quality for each model built. Also compare the qualities of fit and predictive qalities amcing all the differet models respectively. Your comparisons should not be basod on just one parameter. Use all the satistical indicators (mentioned hereabove) provided by Weka to perform the comparisons Dont forget to include all the results based on the 10-fold cross validation andthe lest data set for each model