s_RF: Random Forest Classification and Regression (C, R)

In egenn/rtemis: Machine Learning and Visualization

Random Forest Classification and Regression (C, R)

Description

Train a Random Forest for regression or classification using randomForest

Usage

s_RF(
  x,
  y = NULL,
  x.test = NULL,
  y.test = NULL,
  x.name = NULL,
  y.name = NULL,
  n.trees = 1000,
  autotune = FALSE,
  n.trees.try = 1000,
  stepFactor = 1.5,
  mtry = NULL,
  nodesize = NULL,
  maxnodes = NULL,
  mtryStart = mtry,
  grid.resample.params = setup.resample("kfold", 5),
  metric = NULL,
  maximize = NULL,
  classwt = NULL,
  ifw = TRUE,
  ifw.type = 2,
  upsample = FALSE,
  downsample = FALSE,
  resample.seed = NULL,
  importance = TRUE,
  proximity = FALSE,
  replace = TRUE,
  strata = NULL,
  sampsize = if (replace) nrow(x) else ceiling(0.632 * nrow(x)),
  sampsize.ratio = NULL,
  do.trace = NULL,
  tune.do.trace = FALSE,
  imetrics = FALSE,
  n.cores = rtCores,
  print.tune.plot = FALSE,
  print.plot = FALSE,
  plot.fitted = NULL,
  plot.predicted = NULL,
  plot.theme = rtTheme,
  proximity.tsne = FALSE,
  discard.forest = FALSE,
  tsne.perplexity = 5,
  plot.tsne.train = FALSE,
  plot.tsne.test = FALSE,
  question = NULL,
  verbose = TRUE,
  grid.verbose = verbose,
  outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE),
  ...
)

Arguments

x	Numeric vector or matrix / data frame of features i.e. independent variables
y	Numeric vector of outcome, i.e. dependent variable
x.test	Numeric vector or matrix / data frame of testing set features Columns must correspond to columns in x
y.test	Numeric vector of testing set outcome
x.name	Character: Name for feature set
y.name	Character: Name for outcome
n.trees	Integer: Number of trees to grow. Default = 1000
autotune	Logical: If TRUE, use randomForest::tuneRF to determine mtry
n.trees.try	Integer: Number of trees to train for tuning, if autotune = TRUE
stepFactor	Float: If autotune = TRUE, at each tuning iteration, mtry is multiplied or divided by this value. Default = 1.5
mtry	[gS] Integer: Number of features sampled randomly at each split
nodesize	[gS]: Integer: Minimum size of terminal nodes. Default = 5 (Regression); 1 (Classification)
maxnodes	[gS]: Integer: Maximum number of terminal nodes in a tree. Default = NULL; trees grown to maximum possible
mtryStart	Integer: If autotune = TRUE, start at this value for mtry
grid.resample.params	List: Output of setup.resample defining grid search parameters.
metric	Character: Metric to minimize, or maximize if maximize = TRUE during grid search. Default = NULL, which results in "Balanced Accuracy" for Classification, "MSE" for Regression, and "Coherence" for Survival Analysis.
maximize	Logical: If TRUE, metric will be maximized if grid search is run.
classwt	Vector, Float: Priors of the classes for classification only. Need not add up to 1
ifw	Logical: If TRUE, apply inverse frequency weighting (for Classification only). Note: If weights are provided, ifw is not used.
ifw.type	Integer 0, 1, 2 1: class.weights as in 0, divided by min(class.weights) 2: class.weights as in 0, divided by max(class.weights)
upsample	Logical: If TRUE, upsample training set cases not belonging in majority outcome group
downsample	Logical: If TRUE, downsample majority class to match size of minority class
resample.seed	Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)
importance	Logical: If TRUE, estimate variable relative importance.
proximity	Logical: If TRUE, calculate proximity measure among cases.
replace	Logical: If TRUE, sample cases with replacement during training.
strata	Vector, Factor: Will be used for stratified sampling
sampsize	Integer: Size of sample to draw. In Classification, if strata is defined, this can be a vector of the same length, in which case, corresponding values determine how many cases are drawn from the strata.
sampsize.ratio	Float (0, 1): Heuristic of sorts to increase sensitivity in unbalanced cases. Sample with replacement from minority case to create bootstraps of length N cases. Select ⁠(sampsize.ratio * N minority cases)⁠ cases from majority class.
do.trace	Logical or integer: If TRUE, randomForest will outpout information while it is running. If an integer, randomForest will report progress every this many trees. Default = n.trees/10 if verbose = TRUE
tune.do.trace	Same as do.trace but for tuning, when autotune = TRUE
imetrics	Logical: If TRUE, calculate interpretability metrics (N of trees and N of nodes) and save under the extra field of rtMod
n.cores	Integer: Number of cores to use.
print.tune.plot	Logical: passed to randomForest::tuneRF.
print.plot	Logical: if TRUE, produce plot using mplot3 Takes precedence over plot.fitted and plot.predicted.
plot.fitted	Logical: if TRUE, plot True (y) vs Fitted
plot.predicted	Logical: if TRUE, plot True (y.test) vs Predicted. Requires x.test and y.test
plot.theme	Character: "zero", "dark", "box", "darkbox"
proximity.tsne	Logical: If TRUE, perform t-SNE on proximity matrix. Will be saved under 'extra' field of rtMod. Default = FALSE
discard.forest	Logical: If TRUE, remove forest from rtMod object to save space. Default = FALSE
tsne.perplexity	Numeric: Perplexity parameter for Rtsne::Rtsne
plot.tsne.train	Logical: If TRUE, plot training set tSNE projections
plot.tsne.test	Logical: If TRUE, plot testing set tSNE projections
question	Character: the question you are attempting to answer with this model, in plain language.
verbose	Logical: If TRUE, print summary to screen.
grid.verbose	Logical: Passed to gridSearchLearn
outdir	String, Optional: Path to directory to save output
save.mod	Logical: If TRUE, save all output to an RDS file in outdir save.mod is TRUE by default if an outdir is defined. If set to TRUE, and no outdir is defined, outdir defaults to paste0("./s.", mod.name)
...	Additional arguments to be passed to randomForest::randomForest

Details

If autotue = TRUE, randomForest::tuneRF will be run to determine best mtry value.

Value

rtMod object

Author(s)

E.D. Gennatas

See Also

train_cv for external cross-validation

Other Supervised Learning: s_AdaBoost(), s_AddTree(), s_BART(), s_BRUTO(), s_BayesGLM(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GAM(), s_GAM.default(), s_GAM.formula(), s_GBM(), s_GLM(), s_GLMNET(), s_GLMTree(), s_GLS(), s_H2ODL(), s_H2OGBM(), s_H2ORF(), s_HAL(), s_KNN(), s_LDA(), s_LM(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MARS(), s_MLRF(), s_NBayes(), s_NLA(), s_NLS(), s_NW(), s_PPR(), s_PolyMARS(), s_QDA(), s_QRNN(), s_RFSRC(), s_Ranger(), s_SDA(), s_SGD(), s_SPLS(), s_SVM(), s_TFN(), s_XGBoost(), s_XRF()

Other Tree-based methods: s_AdaBoost(), s_AddTree(), s_BART(), s_C50(), s_CART(), s_CTree(), s_EVTree(), s_GBM(), s_GLMTree(), s_H2OGBM(), s_H2ORF(), s_LMTree(), s_LightCART(), s_LightGBM(), s_MLRF(), s_RFSRC(), s_Ranger(), s_XGBoost(), s_XRF()

Other Ensembles: s_AdaBoost(), s_GBM(), s_Ranger()

egenn/rtemis documentation built on May 4, 2024, 7:40 p.m.