Simon Jackson introduces pipelearner, a tool to help with creating machine learning pipelines:

This post will demonstrate some examples of what pipeleaner can currently do. For example, the Figure below plots the results of a model fitted to 10% to 100% (in 10% increments) of training data in 50 cross-validation pairs. Fitting all of these models takes about four lines of code in pipelearner.

Click through for some very interesting examples.

Related Posts

Combining Plots In R With cowplot

Abdul Majed Raja shows how to use the cowplot library in R to merge together independent plots into a single image: The way it works in cowplot is that, we have assign our individual ggplot-plots as an R object (which is by default of type ggplot). These objects are finally used by cowplot to produce […]

Read More

Where Machine Learning And Econometrics Collide

Dave Giles shares some thoughts on how machine learning and econometrics relate: What is Machine Learning (ML), and how does it differ from Statistics (and hence, implicitly, from Econometrics)? Those are big questions, but I think that they’re ones that econometricians should be thinking about. And if I were starting out in Econometrics today, I’d […]

Read More


January 2017
« Dec Feb »