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.
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")
Type: "surv"
Range: \([0, \infty)\)
Minimize: TRUE
Required prediction: response
mlr3::Measure -> mlr3proba::MeasureSurv -> MeasureSurvMSE
se(logical(1))
If TRUE returns the standard error of the measure.
new()Creates a new instance of this R6 class.
MeasureSurvMSE$new(se = FALSE)
se(logical(1))
If TRUE returns the standard error of the measure.
clone()The objects of this class are cloneable with this method.
MeasureSurvMSE$clone(deep = FALSE)
deepWhether to make a deep clone.
Other survival measures:
mlr_measures_surv.beggC,
mlr_measures_surv.calib_alpha,
mlr_measures_surv.calib_beta,
mlr_measures_surv.chambless_auc,
mlr_measures_surv.cindex,
mlr_measures_surv.gonenC,
mlr_measures_surv.grafSE,
mlr_measures_surv.graf,
mlr_measures_surv.harrellC,
mlr_measures_surv.hung_auc,
mlr_measures_surv.intloglossSE,
mlr_measures_surv.intlogloss,
mlr_measures_surv.loglossSE,
mlr_measures_surv.logloss,
mlr_measures_surv.maeSE,
mlr_measures_surv.mae,
mlr_measures_surv.mseSE,
mlr_measures_surv.nagelk_r2,
mlr_measures_surv.oquigley_r2,
mlr_measures_surv.rmseSE,
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.unoC,
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.maeSE,
mlr_measures_surv.mae,
mlr_measures_surv.mseSE,
mlr_measures_surv.rmseSE,
mlr_measures_surv.rmse