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

See also

Super classes

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

Active bindings

se

(logical(1))
If TRUE returns the standard error of the measure.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

MeasureSurvMSE$new(se = FALSE)

Arguments

se

(logical(1))
If TRUE returns the standard error of the measure.


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.