Hi All,
I'm using number of models such as lm(), tree, randomForest, svm, and nnet for predicting the delays in projects. Also, I computed the sum of squared error for all these models for comparison purposes. However, I want to use other related evaluation criteria such as root mean sum of square error (RMSE) and R Squared for evaluation of these models. My question is that is it possible to compute these criteria (RMSE or R2) for all above-mentioned statistical models. Second, for the lm() we can see the co-efficient values by checking model summary. Is it possible to see the co-efficient for other models such as SVM and neural network? Thanks in advance for the help and support. Many Thanks and Kind Regards -- Muhammad Bilal Research Fellow and Doctoral Researcher, Bristol Enterprise, Research, and Innovation Centre (BERIC), University of the West of England (UWE), Frenchay Campus, Bristol, BS16 1QY muhammad2.bi...@live.uwe.ac.uk<mailto:olugbenga2.akin...@live.uwe.ac.uk> [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
