gbm - Generalized Boosted Model

This guide is designed as a quick-stop reference of how to use some of the more popular machine learning R packages with vivid. In the following example, we use the air quality data for regression.

GBM

The gbm package in R stands for Generalized Boosted Models. It offers an efficient implementation of gradient boosting algorithms for classification, regression, and other machine learning tasks.

library('vivid')
library('gbm')

Regression

# load data
aq <- na.omit(airquality)

# build SVM model
gb <-  gbm(Ozone ~ ., 
           data = aq, 
           distribution = "gaussian")

# vivi matrix
vi <- vivi(data = aq, fit = gb, response = 'Ozone')

PDP

pdpPairs(data = aq, 
         fit =  gb, 
         response = "Ozone", 
         nmax = 50, 
         gridSize = 4,         
         nIce = 10)

Figure 1: Generalized pairs partial dependence plot for a GBM regression fit.