diff --git a/src/tuned_models.jl b/src/tuned_models.jl
index 2eb0c97..a77361e 100644
--- a/src/tuned_models.jl
+++ b/src/tuned_models.jl
@@ -33,7 +33,7 @@ warn_double_spec(arg, model) =
 const ProbabilisticTypes = Union{Probabilistic, MLJBase.MLJModelInterface.ProbabilisticDetector}
 const DeterministicTypes = Union{Deterministic, MLJBase.MLJModelInterface.DeterministicDetector}
 
-mutable struct DeterministicTunedModel{T,M<:DeterministicTypes} <: MLJBase.Deterministic
+mutable struct DeterministicTunedModel{T,M<:DeterministicTypes,L} <: MLJBase.Deterministic
     model::M
     tuning::T  # tuning strategy
     resampling # resampling strategy
@@ -51,9 +51,10 @@ mutable struct DeterministicTunedModel{T,M<:DeterministicTypes} <: MLJBase.Deter
     check_measure::Bool
     cache::Bool
     compact_history::Bool
+    logger::L
 end
 
-mutable struct ProbabilisticTunedModel{T,M<:ProbabilisticTypes} <: MLJBase.Probabilistic
+mutable struct ProbabilisticTunedModel{T,M<:ProbabilisticTypes,L} <: MLJBase.Probabilistic
     model::M
     tuning::T  # tuning strategy
     resampling # resampling strategy
@@ -71,10 +72,11 @@ mutable struct ProbabilisticTunedModel{T,M<:ProbabilisticTypes} <: MLJBase.Proba
     check_measure::Bool
     cache::Bool
     compact_history::Bool
+    logger::L
 end
 
-const EitherTunedModel{T,M} =
-    Union{DeterministicTunedModel{T,M},ProbabilisticTunedModel{T,M}}
+const EitherTunedModel{T,M,L} =
+    Union{DeterministicTunedModel{T,M,L},ProbabilisticTunedModel{T,M,L}}
 
 MLJBase.caches_data_by_default(::Type{<:EitherTunedModel}) = false
 
@@ -279,6 +281,7 @@ function TunedModel(
     check_measure=true,
     cache=true,
     compact_history=true,
+    logger=nothing
     )
 
     # user can specify model as argument instead of kwarg:
@@ -342,6 +345,9 @@ function TunedModel(
     # get the tuning type parameter:
     T = typeof(tuning)
 
+    # get the logger type parameter:
+    L = typeof(logger)
+
     args = (
         model,
         tuning,
@@ -360,12 +366,13 @@ function TunedModel(
         check_measure,
         cache,
         compact_history,
+        logger
     )
 
     if M <: DeterministicTypes
-        tuned_model = DeterministicTunedModel{T,M}(args...)
+        tuned_model = DeterministicTunedModel{T,M,L}(args...)
     elseif M <: ProbabilisticTypes
-        tuned_model = ProbabilisticTunedModel{T,M}(args...)
+        tuned_model = ProbabilisticTunedModel{T,M,L}(args...)
     else
         throw(ERR_MODEL_TYPE)
     end
@@ -591,7 +598,7 @@ function assemble_events!(metamodels,
             end
         end
         # One resampling_machine per task
-         machs = [resampling_machine,
+        machs = [resampling_machine,
                  [machine(Resampler(
                      model= resampling_machine.model.model,
                      resampling    = resampling_machine.model.resampling,
@@ -603,9 +610,9 @@ function assemble_events!(metamodels,
                      repeats       = resampling_machine.model.repeats,
                      acceleration  = resampling_machine.model.acceleration,
                      cache         = resampling_machine.model.cache,
-                     compact       = resampling_machine.model.compact
-                 ), resampling_machine.args...; cache=false) for
-                     _ in 2:length(partitions)]...]
+                     compact       = resampling_machine.model.compact,
+                     logger        = resampling_machine.model.logger),
+                resampling_machine.args...; cache=false) for _ in 2:length(partitions)]...]
 
         @sync for (i, parts) in enumerate(partitions)
             Threads.@spawn begin
@@ -740,8 +747,8 @@ function finalize(tuned_model,
     return fitresult, meta_state, report
 end
 
-function MLJBase.fit(tuned_model::EitherTunedModel{T,M},
-                     verbosity::Integer, data...) where {T,M}
+function MLJBase.fit(tuned_model::EitherTunedModel{T,M,L},
+                     verbosity::Integer, data...) where {T,M,L}
     tuning = tuned_model.tuning
     model = tuned_model.model
     _range = tuned_model.range
@@ -769,6 +776,7 @@ function MLJBase.fit(tuned_model::EitherTunedModel{T,M},
         acceleration  = tuned_model.acceleration_resampling,
         cache         = tuned_model.cache,
         compact       = tuned_model.compact_history,
+        logger        = tuned_model.logger
     )
     resampling_machine = machine(resampler, data...; cache=false)
     history, state = build!(nothing, n, tuning, model, model_buffer, state,
@@ -900,9 +908,9 @@ end
 ## METADATA
 
 MLJBase.is_wrapper(::Type{<:EitherTunedModel}) = true
-MLJBase.supports_weights(::Type{<:EitherTunedModel{<:Any,M}}) where M =
+MLJBase.supports_weights(::Type{<:EitherTunedModel{<:Any,M,L}}) where {M,L} =
     MLJBase.supports_weights(M)
-MLJBase.supports_class_weights(::Type{<:EitherTunedModel{<:Any,M}}) where M =
+MLJBase.supports_class_weights(::Type{<:EitherTunedModel{<:Any,M,L}}) where {M,L} =
     MLJBase.supports_class_weights(M)
 MLJBase.load_path(::Type{<:ProbabilisticTunedModel}) =
     "MLJTuning.ProbabilisticTunedModel"
@@ -914,9 +922,9 @@ MLJBase.package_uuid(::Type{<:EitherTunedModel}) =
 MLJBase.package_url(::Type{<:EitherTunedModel}) =
     "https://github.com/alan-turing-institute/MLJTuning.jl"
 MLJBase.package_license(::Type{<:EitherTunedModel}) = "MIT"
-MLJBase.is_pure_julia(::Type{<:EitherTunedModel{T,M}}) where {T,M} =
+MLJBase.is_pure_julia(::Type{<:EitherTunedModel{T,M,L}}) where {T,M,L} =
     MLJBase.is_pure_julia(M)
-MLJBase.input_scitype(::Type{<:EitherTunedModel{T,M}}) where {T,M} =
+MLJBase.input_scitype(::Type{<:EitherTunedModel{T,M,L}}) where {T,M,L} =
     MLJBase.input_scitype(M)
-MLJBase.target_scitype(::Type{<:EitherTunedModel{T,M}}) where {T,M} =
+MLJBase.target_scitype(::Type{<:EitherTunedModel{T,M,L}}) where {T,M,L} =
     MLJBase.target_scitype(M)