mlr3 (version 0.1.0-9000)

LearnerRegr: Regression Learner

Description

This Learner specializes Learner for regression problems.

Predefined learners can be found in the Dictionary mlr_learners.

Arguments

Format

R6::R6Class object inheriting from Learner.

Construction

l = LearnerRegr$new(id, param_set = ParamSet$new(), param_vals = list(), predict_types = character(),
     feature_types = character(), properties = character(), data_formats = "data.table", packages = character())

For a description of the arguments, see Learner. task_type is set to "regr". Possible values for predict_types are a subset of c("response", "se").

Fields

See Learner.

Methods

All methods of Learner, and additionally:

  • new_prediction(row_ids, truth, response = NULL, prob = NULL) (integer() | character(), numeric(), numeric(), numeric()) -> PredictionRegr Creates a new PredictionRegr object, after performing some basic type checks and transformations. See PredictionRegr for a description of the arguments.

See Also

Example regression learner: regr.rpart.

Other Learner: LearnerClassif, Learner, mlr_learners

Examples

Run this code
# NOT RUN {
# get all regression learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^regr"))
names(lrns)

# get a specific learner from mlr_learners:
lrn = mlr_learners$get("regr.rpart")
print(lrn)
# }

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