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rtemis (version 0.79)

s.H2ORF: Random Forest on H2O [C, R]

Description

Trains a Random Forest model using H2O (http://www.h2o.ai)

Usage

s.H2ORF(x, y = NULL, x.test = NULL, y.test = NULL, x.valid = NULL,
  y.valid = NULL, x.name = NULL, y.name = NULL, ip = "localhost",
  port = 54321, n.trees = 500, max.depth = 20,
  n.stopping.rounds = 50, mtry = -1, nfolds = 0, weights = NULL,
  weights.test = NULL, balance.classes = TRUE, upsample = FALSE,
  na.action = na.fail, n.cores = rtCores, print.plot = TRUE,
  plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"), question = NULL,
  verbose = TRUE, trace = 0, save.mod = FALSE, outdir = NULL, ...)

Arguments

x

Training set features

y

Training set outcome

x.test

Testing set features (Used to evaluate model performance)

y.test

Testing set outcome

x.valid

Validation set features (Used to build model / tune hyperparameters)

y.valid

Validation set outcome

x.name

Character: Name for feature set

y.name

Character: Name for outcome

ip

String: IP address of H2O server. Default = "localhost"

port

Integer: Port to connect to at ip

n.trees

Integer: Number of trees to grow

weights

Numeric vector: Weights for cases. For classification, weights takes precedence over ipw, therefore set weights = NULL if using ipw. Note: If weight are provided, ipw is not used. Leave NULL if setting ipw = TRUE. Default = NULL

upsample

Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Caution: upsample will randomly sample with replacement if the length of the majority class is more than double the length of the class you are upsampling, thereby introducing randomness

na.action

How to handle missing values. See ?na.fail

n.cores

Integer: Number of cores to use

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

String: "zero", "dark", "box", "darkbox"

question

String: the question you are attempting to answer with this model, in plain language.

verbose

Logical: If TRUE, print summary to screen.

trace

Integer: If higher than 0, will print more information to the console. Default = 0

save.mod

Logical. If TRUE, save all output as 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)

outdir

Path to output directory. If defined, will save Predicted vs. True plot, if available, as well as full model output, if save.mod is TRUE

...

Additional parameters to pass to h2o::h2o.randomForest

epochs

Numeric: How many times to iterate through the dataset. Default = 10

Value

rtMod object

See Also

elevate for external cross-validation

Other Supervised Learning: s.ADABOOST, s.ADDTREE, s.BART, s.BAYESGLM, s.BRUTO, s.C50, s.CART, s.CTREE, s.DA, s.ET, s.EVTREE, s.GAM.default, s.GAM.formula, s.GAMSEL, s.GAM, s.GBM3, s.GBM, s.GLMNET, s.GLM, s.GLS, s.H2ODL, s.H2OGBM, s.IRF, s.KNN, s.LDA, s.LM, s.MARS, s.MLRF, s.MXN, s.NBAYES, s.NLA, s.NLS, s.NW, s.POLYMARS, s.PPR, s.PPTREE, s.QDA, s.QRNN, s.RANGER, s.RFSRC, s.RF, s.SGD, s.SPLS, s.SVM, s.TFN, s.XGBLIN, s.XGB

Other Tree-based methods: s.ADABOOST, s.ADDTREE, s.BART, s.C50, s.CART, s.CTREE, s.ET, s.EVTREE, s.GBM3, s.GBM, s.H2OGBM, s.IRF, s.MLRF, s.PPTREE, s.RANGER, s.RFSRC, s.RF, s.XGB