OOBCurve v0.3

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Out of Bag Learning Curve

Provides functions to calculate the out-of-bag learning curve for random forests for any measure that is available in the 'mlr' package. Supported random forest packages are 'randomForest' and 'ranger' and trained models of these packages with the train function of 'mlr'. The main function is OOBCurve() that calculates the out-of-bag curve depending on the number of trees. With the OOBCurvePars() function out-of-bag curves can also be calculated for 'mtry', 'sample.fraction' and 'min.node.size' for the 'ranger' package.

Readme

OOBCurve

With the help of this package the out of bag learning curve for random forests can be created for any measure that is available in the mlr package.

Supported random forest packages are randomForest and ranger and trained models of these packages with the train function of mlr. Available measures can be looked up on the mlr tutorial page.

The main function is OOBCurve that calculates the out-of-bag curve depending on the number of trees. With the OOBCurvePars function out-of-bag curves can also be calculated for mtry, sample.fraction and min.node.size for the ranger package.

Installation:

devtools::install_github("PhilippPro/OOBCurve")

Examples:

library(mlr)
library(ranger)

# Classification
data = getTaskData(sonar.task)
sonar.task = makeClassifTask(data = data, target = "Class")
lrn = makeLearner("classif.ranger", keep.inbag = TRUE, par.vals = list(num.trees = 100))
mod = train(lrn, sonar.task)

# Alternatively use ranger directly
# mod = ranger(Class ~., data = data, num.trees = 100, keep.inbag = TRUE)
# Alternatively use randomForest
# mod = randomForest(Class ~., data = data, ntree = 100, keep.inbag = TRUE)

# Application of the main function
results = OOBCurve(mod, measures = list(mmce, auc, brier), task = sonar.task, data = data)
# Plot the generated results
plot(results$mmce, type = "l", ylab = "oob-mmce", xlab = "ntrees")
plot(results$auc, type = "l", ylab = "oob-auc", xlab = "ntrees")
plot(results$brier, type = "l", ylab = "oob-brier-score", xlab = "ntrees")

# Regression
data = getTaskData(bh.task)
bh.task = makeRegrTask(data = data, target = "medv")
lrn = makeLearner("regr.ranger", keep.inbag = TRUE, par.vals = list(num.trees = 100))
mod = train(lrn, bh.task)

# Application of the main function
results = OOBCurve(mod, measures = list(mse, mae, rsq), task = bh.task, data = data)
# Plot the generated results
plot(results$mse, type = "l", ylab = "oob-mse", xlab = "ntrees")
plot(results$mae, type = "l", ylab = "oob-mae", xlab = "ntrees")
plot(results$rsq, type = "l", ylab = "oob-mae", xlab = "ntrees")

# Use OOBCurvePars for OOBCurve of other hyperparameters
library(mlr)
task = sonar.task

lrn = makeLearner("classif.ranger", predict.type = "prob", num.trees = 1000)
results = OOBCurvePars(lrn, task, measures = list(auc))
plot(results$par.vals, results$performances$auc, type = "l", xlab = "mtry", ylab = "auc")

lrn = makeLearner("classif.ranger", predict.type = "prob", num.trees = 1000, replace = FALSE)
results = OOBCurvePars(lrn, task, pars = "sample.fraction", measures = list(mmce))
plot(results$par.vals, results$performances$mmce, type = "l", xlab = "sample.fraction", ylab = "mmce")

results = OOBCurvePars(lrn, task, pars = "min.node.size", measures = list(mmce))
plot(results$par.vals, results$performances$mmce, type = "l", xlab = "min.node.size", ylab = "mmce")

Functions in OOBCurve

Name Description
OOBCurve Out of Bag Learning curve
OOBCurvePars OOBCurvePars
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Details

Type Package
URL https://github.com/PhilippPro/OOBCurve
BugReports https://github.com/PhilippPro/OOBCurve/issues
License GPL-3
Encoding UTF-8
LazyData yes
ByteCompile yes
Date 2018-08-30
RoxygenNote 6.0.1
NeedsCompilation no
Packaged 2018-08-30 14:43:46 UTC; probst
Repository CRAN
Date/Publication 2018-08-30 15:00:03 UTC

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