mala::home Davide “+mala” Eynard’s website

3Feb/170

Statistical learning with R part 4 (2017): Classification

[This post is part of the Statistical learning with R - 2017 edition series. You might want to check out the previous editions too: 2016, 2015]

So, if you are here you probably have already unpacked the zip file. If not, please check this page before starting.

Try to run classification.R: supposing your current working directory is the one where you unpacked the R files, type

source("classification.R",print.eval=TRUE)

The print.eval parameter is needed to show you the output of some commands such as summary in the context of the source command.

Run the demo and try answering the questions you find there. In some cases you should be able to do that immediately after looking at the results, in others you will first need to add few lines of code to actually get any result. If you find yourself stuck anywhere, all the material you should need is either in the script itself or in the lab notes.

3Feb/170

Statistical learning with R part 3 (2017): Clustering

[This post is part of the Statistical learning with R - 2017 edition series. You might want to check out the previous editions too: 2016, 2015]

So, if you are here you probably have already unpacked the zip file. If not, please check this page before starting.

Try to run clustering.R: supposing your current working directory is the one where you unpacked the R files, type

source("clustering.R",print.eval=TRUE)

The print.eval parameter is needed to show you the output of some commands such as summary in the context of the source command.

Run the demo and try answering the questions you find there. In some cases you should be able to do that immediately after looking at the results, in others you will first need to add few lines of code to actually get any result. If you find yourself stuck anywhere, all the material you should need is either in the script itself or in the lab notes.

1Feb/170

Statistical learning with R part 2 (2017): Correlation vs Causation

[This post is part of the Statistical learning with R - 2017 edition series. You might want to check out the previous editions too: 2016, 2015]

So, if you are here you probably have already unpacked the zip file. If not, please check this page before starting.

Try to run corrcaus.R: supposing your current working directory is the one where you unpacked the R files, type

source("corrcaus.R",print.eval=TRUE)

The print.eval parameter is needed to show you the output of some commands such as summary in the context of the source command.

Run the demo and try answering the questions you find there. In some cases you should be able to do that immediately after looking at the results, in others you will first need to add few lines of code to actually get any result. If you find yourself stuck anywhere, all the material you should need is either in the script itself or in the lab notes.