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,
default, when = "train", requires = NULL) makeNumericVectorLearnerParam(id, len = as.integer(NA),
lower = -Inf, upper = Inf, default, when = "train",
requires = NULL)
makeIntegerLearnerParam(id, lower = -Inf, upper = Inf,
default, when = "train", requires = NULL)
makeIntegerVectorLearnerParam(id, len = as.integer(NA),
lower = -Inf, upper = Inf, default, when = "train",
requires = NULL)
makeDiscreteLearnerParam(id, values, default,
when = "train", requires = NULL)
makeDiscreteVectorLearnerParam(id, len = as.integer(NA),
values, default, when = "train", requires = NULL)
makeLogicalLearnerParam(id, default, when = "train",
requires = NULL)
makeLogicalVectorLearnerParam(id, len = as.integer(NA),
default, when = "train", requires = NULL)
makeUntypedLearnerParam(id, default, when = "train",
requires = NULL)
makeFunctionLearnerParam(id, default, when = "train",
requires = NULL)
character(1)
]
See
Param
.integer(1)
]
See
Param
.numeric
]
See
Param
.numeric
]
See
Param
.vector
| list
]
See
Param
.NULL
| R expression]
See
Param
.character(1)
]
Specifies when
parameter is used in the learner: LearnerParam
].Param
which additionally
stores these elements: [object Object],[object Object],[object Object]