This object stores the predictions returned by a learner of class LearnerSurv.

The `task_type`

is set to `"surv"`

.

Other Prediction:
`PredictionDens`

`mlr3::Prediction`

-> `PredictionSurv`

`truth`

(

`Surv`

)

True (observed) outcome.`crank`

(

`numeric()`

)

Access the stored predicted continuous ranking.`distr`

(VectorDistribution)

Access the stored predicted survival distribution.`lp`

(

`numeric()`

)

Access the stored predicted linear predictor.`response`

(

`numeric()`

)

Access the stored predicted survival time.

`new()`

Creates a new instance of this R6 class.

PredictionSurv$new( task = NULL, row_ids = task$row_ids, truth = task$truth(), crank = NULL, distr = NULL, lp = NULL, response = NULL, check = TRUE )

`task`

(TaskSurv)

Task, used to extract defaults for`row_ids`

and`truth`

.`row_ids`

(

`integer()`

)

Row ids of the predicted observations, i.e. the row ids of the test set.`truth`

(

`survival::Surv()`

)

True (observed) response.`crank`

(

`numeric()`

)

Numeric vector of predicted continuous rankings (or relative risks). One element for each observation in the test set. For a pair of continuous ranks, a higher rank indicates that the observation is more likely to experience the event.`distr`

(VectorDistribution)

VectorDistribution from distr6. Each individual distribution in the vector represents the random variable 'survival time' for an individual observation.`lp`

(

`numeric()`

)

Numeric vector of linear predictor scores. One element for each observation in the test set. \(lp = X\beta\) where \(X\) is a matrix of covariates and \(\beta\) is a vector of estimated coefficients.`response`

(

`numeric()`

)

Numeric vector of predicted survival times. One element for each observation in the test set.`check`

(

`logical(1)`

)

If`TRUE`

, performs argument checks and predict type conversions.

`clone()`

The objects of this class are cloneable with this method.

PredictionSurv$clone(deep = FALSE)

`deep`

Whether to make a deep clone.

if (requireNamespace("rpart", quietly = TRUE)) { library(mlr3) task = tsk("rats") learner = mlr_learners$get("surv.rpart") p = learner$train(task, row_ids = 1:20)$predict(task, row_ids = 21:30) head(as.data.table(p)) }#> row_ids time status crank #> 1: 21 79 FALSE 0.6483971 #> 2: 22 91 FALSE 0.6483971 #> 3: 23 98 FALSE 0.6483971 #> 4: 24 76 FALSE 0.6483971 #> 5: 25 89 FALSE 0.6483971 #> 6: 26 104 FALSE 0.6483971