There are a number of different packages for plotting in Julia, and there's probably one to suit your needs and tastes. This section is a quick introduction to one of them, Plots.jl, which is interesting because it talks to many of the other plotting packages.

Do you know of a good library for gradient boosting tree machine learning? preferably: with good algorithms such as AdaBoost, TreeBoost, AnyBoost, LogitBoost, etc with configurable weak classifiers

class: center, middle, inverse, title-slide # Machine learning in R - Day 2 ## Hands-on workshop at Nationale Nederlanden <html> <div style="float:left"> </div> <hr ...

Get the model using the gbm. For the classification, we use the bernoulli distribution. As the author suggested, normally, we should choose small shrinkage ,such between 0.01 and 0.001; the number of trees, n.trees , is between Summary of the model results, with the importance plot of predictors.

Generalised boosted models, as proposed by and extended by , has been implemented for R as the gbm package by Greg Ridgeway.This is a much more extensive package for boosting than the boost package.

The plots are well written, and sometimes you feel like you're twisting your brain into a knot, trying to figure out the paradoxes. But most importantly it's kind-hearted and beautiful. No doubt 'Doctor Who' will remain a fan-favorite for many years to come.

Using plot_min_depth_distribution, we then get the plot of minimum depth distribution: > GC2_RF_MDD head(GC2_RF_MDD) tree variable minimal_depth 1 1 age 4 2 1 amount 3 3 1 checking 0 4 1 coapp 2 5 1 depends 8 6 1 duration 2 >windows(height=100,width=100) > plot_min_depth_distribution(GC2_RF_MDD,k=nrow(GC2_TestX)) [ 107 ] Random Forests