Train a EVTREE for regression or classification using evtree
s.EVTREE(x, y = NULL, x.test = NULL, y.test = NULL, x.name = NULL,
y.name = NULL, weights = NULL, ipw = TRUE, ipw.type = 2,
control = evtree::evtree.control(), na.action = na.exclude,
print.plot = TRUE, plot.fitted = NULL, plot.predicted = NULL,
plot.theme = getOption("rt.fit.theme", "lightgrid"), question = NULL,
rtclass = NULL, verbose = TRUE, outdir = NULL,
save.mod = ifelse(!is.null(outdir), TRUE, FALSE), ...)
Numeric vector or matrix / data frame of features i.e. independent variables
Numeric vector of outcome, i.e. dependent variable
Numeric vector or matrix / data frame of testing set features
Columns must correspond to columns in x
Numeric vector of testing set outcome
Character: Name for feature set
Character: Name for outcome
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
Logical: If TRUE, apply inverse probability weighting (for Classification only).
Note: If weights
are provided, ipw
is not used. Default = TRUE
Integer 0, 1, 2 1: class.weights as in 0, divided by max(class.weights) 2: class.weights as in 0, divided by min(class.weights) Default = 2
How to handle missing values. See ?na.fail
Logical: if TRUE, produce plot using mplot3
Takes precedence over plot.fitted
and plot.predicted
Logical: if TRUE, plot True (y) vs Fitted
Logical: if TRUE, plot True (y.test) vs Predicted.
Requires x.test
and y.test
String: "zero", "dark", "box", "darkbox"
String: the question you are attempting to answer with this model, in plain language.
String: Class type to use. "S3", "S4", "RC", "R6"
Logical: If TRUE, print summary to screen.
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
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)
Additional arguments to be passed to rpart::rpart
List of additional parameters for the splitting function.
See rpart::rpart("parms")
Float, vector >0: One for each variable in the model.
See rpart::rpart("cost")
Logical: If TRUE, keep a copy of the model. Default = TRUE
Object of class rtMod
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.GAM.default
,
s.GAM.formula
, s.GAMSEL
,
s.GAM
, s.GBM3
,
s.GBM
, s.GLMNET
,
s.GLM
, s.GLS
,
s.H2ODL
, s.H2OGBM
,
s.H2ORF
, 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.GBM3
, s.GBM
,
s.H2OGBM
, s.H2ORF
,
s.IRF
, s.MLRF
,
s.PPTREE
, s.RANGER
,
s.RFSRC
, s.RF
,
s.XGB