Param by adding a few more attributes,
like a default value, whether it refers to a training or a predict function, etc.
makeNumericLearnerParam(id, lower = -Inf, upper = Inf, allow.inf = FALSE, default, when = "train", requires = NULL, tunable = TRUE)
makeNumericVectorLearnerParam(id, len = as.integer(NA), lower = -Inf, upper = Inf, allow.inf = FALSE, default, when = "train", requires = NULL, tunable = TRUE)
makeIntegerLearnerParam(id, lower = -Inf, upper = Inf, default, when = "train", requires = NULL, tunable = TRUE)
makeIntegerVectorLearnerParam(id, len = as.integer(NA), lower = -Inf, upper = Inf, default, when = "train", requires = NULL, tunable = TRUE)
makeDiscreteLearnerParam(id, values, default, when = "train", requires = NULL, tunable = TRUE)
makeDiscreteVectorLearnerParam(id, len = as.integer(NA), values, default, when = "train", requires = NULL, tunable = TRUE)
makeLogicalLearnerParam(id, default, when = "train", requires = NULL, tunable = TRUE)
makeLogicalVectorLearnerParam(id, len = as.integer(NA), default, when = "train", requires = NULL, tunable = TRUE)
makeUntypedLearnerParam(id, default, when = "train", requires = NULL, tunable = TRUE)
makeFunctionLearnerParam(id, default, when = "train", requires = NULL)character(1)]
Name of parameter.numeric]
Lower bounds.
A singe value of length 1 is automatically replicated to len for vector parameters.
Default is -Inf.numeric]
Upper bounds.
A singe value of length 1 is automatically replicated to len for vector parameters.
Default is Inf.logical(1)]
Allow infinite values for numeric and numericvector params to be feasible settings.
Default is FALSE.character(1)]
Specifies when parameter is used in the learner: “train”, “predict” or “both”.
Default is “train”.NULL | call | expression]
States requirements on other parameters' values, so that setting
this parameter only makes sense if its requirements are satisfied (dependent parameter).
Can be an object created either with expression or quote,
the former type is auto-converted into the later.
Only really useful if the parameter is included in a ParamSet.
Note that if your dependent parameter is a logical Boolean you need to verbosely write
requires = quote(a == TRUE) and not requires = quote(a).
Default is NULL which means no requirements.logical(1)]
Is this parameter tunable?
Defining a parameter to be not-tunable allows to mark arguments like, e.g., “verbose” or
other purely technical stuff, and allows them to be excluded from later automatic optimization
procedures that would try to consider all available parameters.
Default is TRUE (except for untyped, function, character and
characterVector) which means it is tunable.integer(1)]
Length of vector parameter.vector | list]
Possible discrete values. Instead of using a vector of atomic values,
you are also allowed to pass a list of quite “complex” R objects,
which are used as discrete choices. If you do the latter,
the elements must be uniquely named, so that the names can be used
as internal representations for the choice.LearnerParam].
Param which additionally stores these elements:
logical(1)]character(1)]