Description Usage Arguments Examples
Creates "Spaghetti" visualization for random forest models to determine the true nature of the effectsCreate Spaghetti visualizations
1 2 3 4 | CreateSpaghettiVis(rf.model, test.df, file, col1 = rgb(1, 0, 0, 1),
col0 = rgb(0, 0, 0, 1), point.cex = 1, n.sample = 50,
loess.span = .80,
truncate.quantile.upper = .98, truncate.quantile.lower = .02)
|
rf.model |
fitted random forest model |
test.df |
hold out data set |
file |
output location |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | library(glassbox)
data(testdata)
library(randomForest)
rf.model <- randomForest(y ~ x + z, data=testdata)
CreateSpaghettiVis(rf.model, testdata, "~/Google Drive/filename.pdf",
col1=rgb(0, 0, 0, .05), n.sample = 1000)
# Binary case
testdata$w <- runif(1000) <= plogis(.5 * testdata$x - .7 * testdata$z)
train <- testdata[1:800, ]
test <- testdata[801:1000, ]
rf.model.2 <- randomForest(as.factor(w) ~ x + z, train)
CreateSpaghettiVis(rf.model.2, test, "~/Google Drive/filename2.pdf",
col0=rgb(1, 0, 0, .05), col1=rgb(0, 0, 0, 1), n.sample=200)
|
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