View source: R/ResampleResult.R
autoplot.ResampleResult | R Documentation |
Generates plots for mlr3::ResampleResult, depending on argument type
:
"boxplot"
(default): Boxplot of performance measures.
"histogram"
: Histogram of performance measures.
"roc"
: ROC curve (1 - specificity on x, sensitivity on y).
The predictions of the individual mlr3::Resamplings are merged prior to
calculating the ROC curve (micro averaged). Requires package
precrec.
Additional arguments will be passed down to the respective autoplot()
function
in package precrec. Arguments calc_avg
and cb_alpha
are passed to
precrec::evalmod()
.
"prc"
: Precision recall curve. See "roc"
.
"prediction"
: Plots the learner prediction for a grid of points.
Needs models to be stored. Set store_models = TRUE
for
[mlr3::resample]
.
For classification, we support tasks with exactly two features and
learners with predict_type=
set to "response"
or "prob"
.
For regression, we support tasks with one or two features.
For tasks with one feature we can print confidence bounds if the predict
type of the learner was set to "se"
.
For tasks with two features the predict type will be ignored.
## S3 method for class 'ResampleResult' autoplot(object, type = "boxplot", measure = NULL, predict_sets = "test", ...)
object |
(mlr3::ResampleResult). |
type |
(character(1)): |
measure |
(mlr3::Measure) |
predict_sets |
( |
... |
( |
ggplot2::ggplot()
object.
The theme_mlr3()
and viridis color maps are applied by default to all
autoplot()
methods. To change this behavior set
options(mlr3.theme = FALSE)
.
Saito T, Rehmsmeier M (2017). “Precrec: fast and accurate precision-recall and ROC curve calculations in R.” Bioinformatics, 33(1), 145-147. doi: 10.1093/bioinformatics/btw570.
if (requireNamespace("mlr3")) { library(mlr3) library(mlr3viz) task = tsk("sonar") learner = lrn("classif.rpart", predict_type = "prob") resampling = rsmp("cv") object = resample(task, learner, resampling) head(fortify(object)) # Default: boxplot autoplot(object) # Histogram autoplot(object, type = "histogram", bins = 30) # ROC curve, averaged over resampling folds: autoplot(object, type = "roc") # ROC curve of joint prediction object: autoplot(object$prediction(), type = "roc") # Precision Recall Curve autoplot(object, type = "prc") # Prediction Plot task = tsk("iris")$select(c("Sepal.Length", "Sepal.Width")) resampling = rsmp("cv", folds = 3) object = resample(task, learner, resampling, store_models = TRUE) autoplot(object, type = "prediction") }
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