bartMachineVivid.RmdThis 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 examples, we use the air quality data for regression
and the iris data for classification.
The bartMachine package in R provides an implementation
of Bayesian Additive Regression Trees (BART), a non-parametric Bayesian
model which uses decision trees as the underlying model. To begin we
load the packages and data.
# load data 
aq <- na.omit(airquality)
# build bartMachine model
bm <- bartMachine(X = aq[,2:6], 
                  y = aq[,1], 
                  num_trees = 20,
                  num_burn_in = 100,
                  num_iterations_after_burn_in = 500)
# vivi matrix
vi <- vivi(data = aq, fit = bm, response = 'Ozone')
viviHeatmap(mat = vi)
pdpPairs(data = aq, 
         fit =  bm, 
         response = "Ozone", 
         nmax = 500, 
         gridSize = 10,         
         nIce = 50)
# Get data and only use 2 factors
data(iris)
iris2 = iris[51:150,]
iris2$Species = factor(iris2$Species)
bm <- build_bart_machine(iris2[ ,1:4], iris2$Species,
                         num_trees = 20,
                         num_burn_in = 100,
                         num_iterations_after_burn_in = 500)
# vivid
vi <- vivi(data = iris2, fit = bm, response = 'Species')
viviHeatmap(mat = vi)
pdpPairs(data = iris2, 
         fit =  bm, 
         response = "Species", 
         nmax = 500, 
         gridSize = 10,         
         nIce = 50)