By design GNU R uses lexical scoping. Fortunately it allows for at least two ways to simulate dynamic scoping.Let us start with the example code and next analyze it:x <- "global"f1 <- function() cat("f1:", x, "\n")f2 <- function() cat("f2:", e...

By design GNU R uses lexical scoping. Fortunately it allows for at least two ways to simulate dynamic scoping.Let us start with the example code and next analyze it:x <- "global"f1 <- function() cat("f1:", x, "\n")f2 <- function() cat("f2:", e...

At MSR this week, we had two very good talks on algorithmic methods for tuning the hyperparameters of machine learning models. Selecting appropriate settings for hyperparameters is a constant problem in machine learning, which is somewhat surprising given how much expertise the machine learning community has in optimization theory. I suspect there’s interesting psychological and

Standard nonlinear regression assumes homoscedastic data, that is, all response values are distributed normally. In case of heteroscedastic data (i.e. when the variance is dependent on the magnitude of the data), weighting the fit is essential. In nls (or nlsLM of the minpack.lm package), weighting can be conducted by two different methods: 1) by supplying

The Tenth Australasian Data Mining Conference (AusDM 2012) Sydney, Australia, 5-7 December 2012 http://ausdm12.togaware.com/ The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. This year’s conference, AusDM’12, co-hosted … Continue reading →

In my last post I have shown a solution to classical sorting problem in R. So I thought that this time it would be nice to generate a strategy for playing Mastermind using R.It was shown by D.E. Knuth that Mastermind code can be bro...

In my last post, I considered the shifts in two interestingness measures as possible tools for selecting variables in classification problems. Specifically, I considered the Gini and Shannon interestingness measures applied to the 22 categorical mushroom characteristics from the UCI mushroom dataset. The proposed variable selection strategy was to compare these values when computed from only edible mushrooms...

by Yanchang Zhao, RDataMining.com There are some nice slides and R code examples on Data Mining and Exploration at http://www.inf.ed.ac.uk/teaching/courses/dme/, which are listed below. PDF Slides: - Overview of Data Mining http://www.inf.ed.ac.uk/teaching/courses/dme/2012/slides/datamining_intro4up.pdf - Visualizing Data http://www.inf.ed.ac.uk/teaching/courses/dme/2012/slides/visualisation4up.pdf - Decision trees http://www.inf.ed.ac.uk/teaching/courses/dme/2012/slides/classification4up.pdf … Continue reading →