diff --git a/src/builtins/Constant.jl b/src/builtins/Constant.jl
index 83a39b4d..65d0d12b 100644
--- a/src/builtins/Constant.jl
+++ b/src/builtins/Constant.jl
@@ -11,17 +11,17 @@ struct ConstantRegressor{D<:Distributions.Sampleable} <: Probabilistic
     distribution_type::Type{D}
 end
 
-ConstantRegressor(; distribution_type=Distributions.Normal) =
+ConstantRegressor(; distribution_type = Distributions.Normal) =
     ConstantRegressor(distribution_type)
 
-function MLJBase.fit(::ConstantRegressor{D}, verbosity::Int, X, y) where D
+function MLJBase.fit(::ConstantRegressor{D}, verbosity::Int, X, y) where {D}
     fitresult = Distributions.fit(D, y)
-    cache     = nothing
-    report    = NamedTuple()
+    cache = nothing
+    report = NamedTuple()
     return fitresult, cache, report
 end
 
-MLJBase.fitted_params(::ConstantRegressor, fitresult) = (target_distribution=fitresult,)
+MLJBase.fitted_params(::ConstantRegressor, fitresult) = (target_distribution = fitresult,)
 
 MLJBase.predict(::ConstantRegressor, fitresult, Xnew) = fill(fitresult, nrows(Xnew))
 
@@ -33,12 +33,13 @@ struct DeterministicConstantRegressor <: Deterministic end
 
 function MLJBase.fit(::DeterministicConstantRegressor, verbosity::Int, X, y)
     fitresult = mean(y)
-    cache     = nothing
-    report    = NamedTuple()
+    cache = nothing
+    report = NamedTuple()
     return fitresult, cache, report
 end
 
-MLJBase.predict(::DeterministicConstantRegressor, fitresult, Xnew) = fill(fitresult, nrows(Xnew))
+MLJBase.predict(::DeterministicConstantRegressor, fitresult, Xnew) =
+    fill(fitresult, nrows(Xnew))
 
 ##
 ## THE CONSTANT CLASSIFIER
@@ -56,14 +57,14 @@ obtain the training target mode instead.
 struct ConstantClassifier <: Probabilistic end
 
 # here `args` is `y` or `y, w`:
-function MLJBase.fit(::ConstantClassifier, verbosity::Int, X, y, w=nothing)
+function MLJBase.fit(::ConstantClassifier, verbosity::Int, X, y, w = nothing)
     fitresult = Distributions.fit(MLJBase.UnivariateFinite, y, w)
-    cache     = nothing
-    report    = NamedTuple
+    cache = nothing
+    report = NamedTuple
     return fitresult, cache, report
 end
 
-MLJBase.fitted_params(::ConstantClassifier, fitresult) = (target_distribution=fitresult,)
+MLJBase.fitted_params(::ConstantClassifier, fitresult) = (target_distribution = fitresult,)
 
 MLJBase.predict(::ConstantClassifier, fitresult, Xnew) = fill(fitresult, nrows(Xnew))
 
@@ -76,51 +77,65 @@ struct DeterministicConstantClassifier <: Deterministic end
 function MLJBase.fit(::DeterministicConstantClassifier, verbosity::Int, X, y)
     # dump missing target values and make into a regular array:
     fitresult = mode(skipmissing(y) |> collect) # a CategoricalValue or CategoricalString
-    cache     = nothing
-    report    = NamedTuple()
+    cache = nothing
+    report = NamedTuple()
     return fitresult, cache, report
 end
 
-MLJBase.predict(::DeterministicConstantClassifier, fitresult, Xnew) = fill(fitresult, nrows(Xnew))
+MLJBase.predict(::DeterministicConstantClassifier, fitresult, Xnew) =
+    fill(fitresult, nrows(Xnew))
 
 ##
 ## METADATA
 ##
 
 metadata_pkg.(
-    (ConstantRegressor, ConstantClassifier,
-     DeterministicConstantRegressor, DeterministicConstantClassifier),
-    name       = "MLJModels",
-    uuid       = "d491faf4-2d78-11e9-2867-c94bc002c0b7",
-    url        = "https://github.com/alan-turing-institute/MLJModels.jl",
-    julia      = true,
-    license    = "MIT",
-    is_wrapper = false)
-
-metadata_model(ConstantRegressor,
-    input   = Table(Scientific),
-    target  = AbstractVector{Continuous},
+    (
+        ConstantRegressor,
+        ConstantClassifier,
+        DeterministicConstantRegressor,
+        DeterministicConstantClassifier,
+    ),
+    name = "MLJModels",
+    uuid = "d491faf4-2d78-11e9-2867-c94bc002c0b7",
+    url = "https://github.com/alan-turing-institute/MLJModels.jl",
+    julia = true,
+    license = "MIT",
+    is_wrapper = false,
+)
+
+metadata_model(
+    ConstantRegressor,
+    input = Table(Scientific),
+    target = AbstractVector{Continuous},
     weights = false,
-    descr   = "Constant regressor (Probabilistic).",
-    path    = "MLJModels.ConstantRegressor")
-
-metadata_model(DeterministicConstantRegressor,
-    input   = Table(Scientific),
-    target  = AbstractVector{Continuous},
+    descr = "Constant regressor (Probabilistic).",
+    path = "MLJModels.ConstantRegressor",
+)
+
+metadata_model(
+    DeterministicConstantRegressor,
+    input = Table(Scientific),
+    target = AbstractVector{Continuous},
     weights = false,
-    descr   = "Constant regressor (Deterministic).",
-    path    = "MLJModels.DeterministicConstantRegressor")
-
-metadata_model(ConstantClassifier,
-    input   = Table(Scientific),
-    target  = AbstractVector{<:Finite},
+    descr = "Constant regressor (Deterministic).",
+    path = "MLJModels.DeterministicConstantRegressor",
+)
+
+metadata_model(
+    ConstantClassifier,
+    input = Table(Scientific),
+    target = AbstractVector{<:Finite},
     weights = true,
-    descr   = "Constant classifier (Probabilistic).",
-    path    = "MLJModels.ConstantClassifier")
-
-metadata_model(DeterministicConstantClassifier,
-    input   = Table(Scientific),
-    target  = AbstractVector{<:Finite},
+    descr = "Constant classifier (Probabilistic).",
+    path = "MLJModels.ConstantClassifier",
+)
+
+metadata_model(
+    DeterministicConstantClassifier,
+    input = Table(Scientific),
+    target = AbstractVector{<:Finite},
     weights = false,
-    descr   = "Constant classifier (Deterministic).",
-    path    = "MLJModels.DeterministicConstantClassifier")
+    descr = "Constant classifier (Deterministic).",
+    path = "MLJModels.DeterministicConstantClassifier",
+)