mlr3proba (version 0.4.9)

mlr_measures_surv.mse: Mean Squared Error Survival Measure

Description

Calculates the mean squared error (MSE).

The MSE is defined by $$\frac{1}{n} \sum ((t - \hat{t})^2)$$ where \(t\) is the true value and \(\hat{t}\) is the prediction.

Censored observations in the test set are ignored.

Dictionary

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

MeasureSurvMSE$new()
mlr_measures$get("surv.mse")
msr("surv.mse")

Meta Information

• Type: "surv"

• Range: \([0, \infty)\)

• Minimize: TRUE

• Required prediction: response

Super classes

mlr3::Measure -> mlr3proba::MeasureSurv -> MeasureSurvMSE

Methods

Public methods

• MeasureSurvMSE$new()

• MeasureSurvMSE$clone()

• mlr3::Measure$aggregate()

• mlr3::Measure$format()

• mlr3::Measure$help()

• mlr3::Measure$score()

• mlr3proba::MeasureSurv$print()

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSurvMSE$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

MeasureSurvMSE$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other survival measures: mlr_measures_surv.calib_alpha, mlr_measures_surv.calib_beta, mlr_measures_surv.chambless_auc, mlr_measures_surv.cindex, mlr_measures_surv.dcalib, mlr_measures_surv.graf, mlr_measures_surv.hung_auc, mlr_measures_surv.intlogloss, mlr_measures_surv.logloss, mlr_measures_surv.mae, mlr_measures_surv.nagelk_r2, mlr_measures_surv.oquigley_r2, mlr_measures_surv.rcll, mlr_measures_surv.rmse, mlr_measures_surv.schmid, mlr_measures_surv.song_auc, mlr_measures_surv.song_tnr, mlr_measures_surv.song_tpr, mlr_measures_surv.uno_auc, mlr_measures_surv.uno_tnr, mlr_measures_surv.uno_tpr, mlr_measures_surv.xu_r2

Other response survival measures: mlr_measures_surv.mae, mlr_measures_surv.rmse