Description Usage Arguments Value See Also Examples
View source: R/classification.R
Standard random forests algorithm serves as a baseline model
1 2 3 4 5 6 7 8 9 10 11 12 |
train.ds |
A data frame with training data and outcome labels |
holdout.ds |
A data frame with holdout data and outcome labels |
validation.ds |
A data frame with validation data and outcome labels |
label |
A character vector of the outcome variable column name |
rf.ntree |
An integer the number of trees in the random forest |
rf.mtry |
An integer the number of variables sampled at each random forest node split |
is.simulated |
Is the data simulated (or real?) |
signal.names |
A character vector of signal names in simulated data |
save.file |
A character vector for results filename or NULL to skip |
verbose |
A flag indicating whether verbose output be sent to stdout |
A list containing:
data frame of results, a row for each update
melted results data frame for plotting
number of variables detected correctly in each data set
total elapsed time
Other classification:
epistasisRank()
,
getImportanceScores()
,
originalThresholdout()
,
privateEC()
,
privateRF()
,
xgboostRF()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | num.samples <- 100
num.variables <- 100
pct.signals <- 0.1
label <- "class"
sim.data <- createSimulation(num.variables = num.variables,
num.samples = num.samples,
pct.signals = pct.signals,
label = label,
sim.type = "mainEffect",
pct.train = 1 / 3,
pct.holdout = 1 / 3,
pct.validation = 1 / 3,
verbose = FALSE)
rra.results <- standardRF(train.ds=sim.data$train,
holdout.ds=sim.data$holdout,
validation.ds=sim.data$validation,
label=sim.data$label,
is.simulated=TRUE,
verbose=FALSE,
signal.names=sim.data$signal.names)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.