diff --git a/R/Measure.R b/R/Measure.R
index ff45b64d8..5c58b85e8 100644
--- a/R/Measure.R
+++ b/R/Measure.R
@@ -194,7 +194,7 @@ Measure = R6Class("Measure",
     #'
     #' @return `numeric(1)`.
     score = function(prediction, task = NULL, learner = NULL, train_set = NULL) {
-      assert_measure(self, task = task, learner = learner, prediction = prediction)
+      assert_scorable(self, task = task, learner = learner, prediction = prediction)
       assert_prediction(prediction, null.ok = "requires_no_prediction" %nin% self$properties)
 
       # check should be added to assert_measure()
@@ -395,7 +395,7 @@ score_measures = function(obj, measures, reassemble = TRUE, view = NULL, iters =
   tmp = unique(tab, by = c("task_hash", "learner_hash"))[, c("task", "learner"), with = FALSE]
 
   for (measure in measures) {
-    pmap(tmp, assert_measure, measure = measure)
+    pmap(tmp, assert_scorable, measure = measure)
 
     score = pmap_dbl(tab[, c("task", "learner", "resampling", "iteration", "prediction"), with = FALSE],
       function(task, learner, resampling, iteration, prediction) {
diff --git a/R/assertions.R b/R/assertions.R
index f4c76fa41..fa6618dbd 100644
--- a/R/assertions.R
+++ b/R/assertions.R
@@ -223,14 +223,6 @@ assert_measure = function(measure, task = NULL, learner = NULL, prediction = NUL
 
   if (!is.null(learner)) {
 
-    if ("requires_model" %in% measure$properties && is.null(learner$model)) {
-      stopf("Measure '%s' requires the trained model", measure$id)
-    }
-
-    if ("requires_model" %in% measure$properties && is_marshaled_model(learner$model)) {
-      stopf("Measure '%s' requires the trained model, but model is in marshaled form", measure$id)
-    }
-
     if (!is_scalar_na(measure$task_type) && measure$task_type != learner$task_type) {
       stopf("Measure '%s' is not compatible with type '%s' of learner '%s'",
         measure$id, learner$task_type, learner$id)
@@ -263,6 +255,21 @@ assert_measure = function(measure, task = NULL, learner = NULL, prediction = NUL
   invisible(measure)
 }
 
+#' @export
+#' @param measure ([Measure]).
+#' @param prediction ([Prediction]).
+#' @rdname mlr_assertions
+assert_scorable = function(measure, task, learner, prediction = NULL, .var.name = vname(measure)) {
+  if ("requires_model" %in% measure$properties && is.null(learner$model)) {
+    stopf("Measure '%s' requires the trained model", measure$id)
+  }
+
+  if ("requires_model" %in% measure$properties && is_marshaled_model(learner$model)) {
+    stopf("Measure '%s' requires the trained model, but model is in marshaled form", measure$id)
+  }
+
+  assert_measure(measure, task = task, learner = learner, prediction = prediction, .var.name = .var.name)
+}
 
 #' @export
 #' @param measures (list of [Measure]).