Koos van Strien moves from Python to R to run an xgboost algorithm:

Note that the parameters of xgboost used here fall in three categories:

  • General parameters

    • nthread (number of threads used, here 8 = the number of cores in my laptop)
  • Booster parameters

    • max.depth (of tree)
    • eta
  • Learning task parameters

    • objective: type of learning task (softmax for multiclass classification)
    • num_class: needed for the “softmax” algorithm: how many classes to predict?
  • Command Line Parameters

    • nround: number of rounds for boosting

Read the whole thing.

Related Posts

Using wrapr For A Consistent Pipe With ggplot2

John Mount shows how you can use the wrapr pipe to perform data processing and building a ggplot2 visual: Now we can run a single pipeline that combines data processing steps and ggplot plot construction. data.frame(x = 1:20) %.>% mutate(., y = cos(3*x)) %.>% ggplot(., aes(x = x, y = y)) %.>% geom_point() %.>% geom_line() %.>% ggtitle("piped ggplot2") Check […]

Read More

Using R To Hit Azure ML From Power BI

Leila Etaati shows how you can use R to hit an Azure ML endpoint to populate a data set in Power BI: You need to create a model in Azure ML Studio and create a web service for it. The traditional example in Predict a passenger on Titanic ship is going to survived or not? […]

Read More


September 2016
« Aug Oct »