For a 1.1.1 release

src/resampling.jl

@@ -1591,8 +1591,15 @@ function MLJModelInterface.update(
     mach, e = fitresult
     train_test_rows = e.train_test_rows
 
-    measures = e.measure
-    operations = e.operation
+    # since `resampler.model` could have changed, so might the actual measures and
+    # operations that should be passed to the (low level) `evaluate!`:
+    measures = _actual_measures(resampler.measure, resampler.model)
+    operations = _actual_operations(
+        resampler.operation,
+        measures,
+        resampler.model,
+        verbosity
+    )
 
     # update the model:
     mach2 = _update!(mach, resampler.model)

test/resampling.jl

@@ -1,5 +1,3 @@
-#module TestResampling
-
 using Distributed
 import ComputationalResources: CPU1, CPUProcesses, CPUThreads
 using .TestUtilities
@@ -876,5 +874,21 @@ end
     @test contains(printed_evaluations, "N/A")
 end
 
-#end
+@testset_accelerated "issue with Resampler #954" acceleration begin
+    knn = KNNClassifier()
+    cnst =DeterministicConstantClassifier()
+    X, y = make_blobs(10)
+
+    resampler = MLJBase.Resampler(
+        ;model=knn,
+        measure=accuracy,
+        operation=nothing,
+        acceleration,
+    )
+    mach = machine(resampler, X, y) |> fit!
+
+    resampler.model = cnst
+    fit!(mach)
+end
+
 true