mlr_measures_regr.rsq: R-Squared

In mlr3: Machine Learning in R - Next Generation

R-Squared

Description

Measure to compare true observed response with predicted response in regression tasks.

Details

R Squared is defined as

1 - \frac{\sum_{i=1}^n \left( t_i - r_i \right)^2}{\sum_{i=1}^n \left( t_i - \bar{t} \right)^2},

where \bar{t} = \sum_{i=1}^n t_i.

Also known as coefficient of determination or explained variation. Subtracts the mlr3measures::rse() from 1, hence it compares the squared error of the predictions relative to a naive model predicting the mean.

This measure is undefined for constant t.

Dictionary

This Measure can be instantiated via the dictionary mlr_measures or with the associated sugar function msr():

mlr_measures$get("regr.rsq")
msr("regr.rsq")

Meta Information

• Task type: “regr”

• Range: (-\infty, 1]

• Minimize: FALSE

• Average: macro

• Required Prediction: “response”

• Required Packages: mlr3

Parameters

Empty ParamSet

Super classes

mlr3::Measure -> mlr3::MeasureRegr -> MeasureRSQ

Methods

Public methods

• MeasureRegrRSQ$new()

• MeasureRegrRSQ$clone()

Inherited methods

• mlr3::Measure$aggregate()
• mlr3::Measure$format()
• mlr3::Measure$help()
• mlr3::Measure$print()
• mlr3::Measure$score()

---

Method new()

Creates a new instance of this R6 class.

Usage

MeasureRegrRSQ$new(pred_set_mean = TRUE)

Arguments

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.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureRegrRSQ$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

• 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_selected_features

mlr3 documentation built on Oct. 18, 2024, 5:11 p.m.