This object wraps the predictions returned by a learner of class LearnerRegr, i.e.
the predicted response and standard error.
Additionally, probability distributions implemented in package distr6
are supported.
See also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html
Package mlr3viz for some generic visualizations.
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
Other Prediction:
Prediction
,
PredictionClassif
Super class
mlr3::Prediction
-> PredictionRegr
Active bindings
response
(
numeric()
)
Access the stored predicted response.se
(
numeric()
)
Access the stored standard error.quantiles
(
matrix()
)
Matrix of predicted quantiles. Observations are in rows, quantile (in ascending order) in columns.distr
(
VectorDistribution
)
Access the stored vector distribution. Requires packagedistr6
(in repository https://raphaels1.r-universe.dev) .
Methods
Method new()
Creates a new instance of this R6 class.
Usage
PredictionRegr$new(
task = NULL,
row_ids = task$row_ids,
truth = task$truth(),
response = NULL,
se = NULL,
quantiles = NULL,
distr = NULL,
check = TRUE
)
Arguments
task
(TaskRegr)
Task, used to extract defaults forrow_ids
andtruth
.row_ids
(
integer()
)
Row ids of the predicted observations, i.e. the row ids of the test set.truth
(
numeric()
)
True (observed) response.response
(
numeric()
)
Vector of numeric response values. One element for each observation in the test set.se
(
numeric()
)
Numeric vector of predicted standard errors. One element for each observation in the test set.quantiles
(
matrix()
)
Numeric matrix of predicted quantiles. One row per observation, one column per quantile.distr
(
VectorDistribution
)VectorDistribution
from package distr6 (in repository https://raphaels1.r-universe.dev). Each individual distribution in the vector represents the random variable 'survival time' for an individual observation.check
(
logical(1)
)
IfTRUE
, performs some argument checks and predict type conversions.
Examples
task = tsk("boston_housing")
learner = lrn("regr.featureless", predict_type = "se")
p = learner$train(task)$predict(task)
p$predict_types
#> [1] "response" "se"
head(as.data.table(p))
#> row_ids truth response se
#> <int> <num> <num> <num>
#> 1: 1 24.0 22.52885 9.182176
#> 2: 2 21.6 22.52885 9.182176
#> 3: 3 34.7 22.52885 9.182176
#> 4: 4 33.4 22.52885 9.182176
#> 5: 5 36.2 22.52885 9.182176
#> 6: 6 28.7 22.52885 9.182176