mlr_measures_regr.rqr | R Documentation |
Measure to compare true observed response with predicted quantiles in regression tasks.
R^1(\alpha)
is defined as
1 - \frac{\sum_{i=1}^n \rho_\alpha \left( t_i - r_i(\alpha) \right)}{\sum_{i=1}^n \rho_\alpha \left( t_i - q_{\alpha} \right)},
where for a quantile \alpha
, \rho_\alpha
is the pinball function, r_i(\alpha)
are the predictions
for the quantile and q_{\alpha}
is the empirical \alpha
-quantile of the test or training data.
R^1(\alpha)
is analogous to R^2
for regression tasks.
It compares the pinball function of the predictions relative to a naive model predicting the empirical quantile.
This measure is undefined for constant t
.
This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr()
:
mlr_measures$get("regr.rqr") msr("regr.rqr")
Task type: “regr”
Range: (-\infty, 1]
Minimize: FALSE
Average: macro
Required Prediction: “quantiles”
Required Packages: mlr3
Id | Type | Default | Range |
alpha | numeric | - | [0, 1] |
mlr3::Measure
-> mlr3::MeasureRegr
-> MeasureRQR
new()
Creates a new instance of this R6 class.
MeasureRegrRQR$new(alpha = 0.5, pred_set_mean = TRUE)
alpha
numeric(1)
The quantile for which to compute the measure.
Must be one of the quantiles that the Learner was trained on.
pred_set_mean
logical(1)
If TRUE
, the mean of the true values is calculated on the prediction set.
If FALSE
, the mean of the true values is calculated on the training set.
clone()
The objects of this class are cloneable with this method.
MeasureRegrRQR$clone(deep = FALSE)
deep
Whether to make a deep clone.
Koenker, Roger, Machado, F. JA (1999). “Goodness of Fit and Related Inference Processes for Quantile Regression.” Journal of the American Statistical Association, 94(448), 1296–1310. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.1999.10473882")}.
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-eval
Package mlr3measures for the scoring functions.
Dictionary of Measures: mlr_measures
as.data.table(mlr_measures)
for a table of available Measures in the running session (depending on the loaded packages).
Extension packages for additional task types:
mlr3proba for probabilistic supervised regression and survival analysis.
mlr3cluster for unsupervised clustering.
Other Measure:
Measure
,
MeasureClassif
,
MeasureRegr
,
MeasureSimilarity
,
mlr_measures
,
mlr_measures_aic
,
mlr_measures_bic
,
mlr_measures_classif.costs
,
mlr_measures_debug_classif
,
mlr_measures_elapsed_time
,
mlr_measures_internal_valid_score
,
mlr_measures_oob_error
,
mlr_measures_regr.pinball
,
mlr_measures_regr.rsq
,
mlr_measures_selected_features
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