diff --git a/Project.toml b/Project.toml
index 5cbda20..dffd492 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "MLJXGBoostInterface"
 uuid = "54119dfa-1dab-4055-a167-80440f4f7a91"
 authors = ["Anthony D. Blaom <anthony.blaom@gmail.com>"]
-version = "0.3.6"
+version = "0.3.7"
 
 [deps]
 MLJModelInterface = "e80e1ace-859a-464e-9ed9-23947d8ae3ea"
diff --git a/src/MLJXGBoostInterface.jl b/src/MLJXGBoostInterface.jl
index d125ad5..113cd6c 100644
--- a/src/MLJXGBoostInterface.jl
+++ b/src/MLJXGBoostInterface.jl
@@ -136,10 +136,11 @@ end
 function MMI.fit(model::XGBoostAbstractRegressor, verbosity::Integer, X, y)
     dm = DMatrix(MMI.matrix(X), float(y))
     b = xgboost(dm; kwargs(model, verbosity, model.objective)...)
-    (b, nothing, (features=_feature_names(X, dm),))
+    # first return value is a tuple for consistancy with classifier case
+    ((b, nothing), nothing, (features=_feature_names(X, dm),))
 end
 
-MMI.predict(model::XGBoostAbstractRegressor, fitresult, Xnew) = XGB.predict(fitresult, Xnew)
+MMI.predict(model::XGBoostAbstractRegressor, (booster, _), Xnew) = XGB.predict(booster, Xnew)
 
 
 eval(modelexpr(:XGBoostCount, :XGBoostAbstractRegressor, "count:poisson", :validate_count_objective))
@@ -189,13 +190,9 @@ _save(fr; kw...) = XGB.save(fr, Vector{UInt8}; kw...)
 
 _restore(fr) = XGB.load(Booster, fr)
 
-MMI.save(::XGBoostAbstractRegressor, fr; kw...) = _save(fr; kw...)
+MMI.save(::XGTypes, fr; kw...) = (_save(fr[1]; kw...), fr[2])
 
-MMI.restore(::XGBoostAbstractRegressor, fr; kw...) = _restore(fr)
-
-MMI.save(::XGBoostClassifier, fr; kw...) = (_save(fr[1]; kw...), fr[2])
-
-MMI.restore(::XGBoostClassifier, fr) = (_restore(fr[1]), fr[2])
+MMI.restore(::XGTypes, fr) = (_restore(fr[1]), fr[2])
 
 MLJModelInterface.reports_feature_importances(::Type{<:XGBoostAbstractRegressor}) = true
 MLJModelInterface.reports_feature_importances(::Type{<:XGBoostAbstractClassifier}) = true