Bump scikit-learn, add todo's for future scikit-learn versions

CondaPkg.toml

@@ -1,4 +1,4 @@
 
 [deps.scikit-learn]
 channel = "conda-forge"
-version = ">=1.4, <1.5"
+version = ">=1.4, <1.6"

src/models/ensemble.jl

@@ -36,6 +36,7 @@ const AdaBoostClassifier_ = sken(:AdaBoostClassifier)
     estimator::Any    = nothing
     n_estimators::Int      = 50::(_ > 0)
     learning_rate::Float64 = 1.0::(_ > 0)
+    #TODO: Remove `SAMME.R` in v1.6
     algorithm::String      = "SAMME.R"::(_ in ("SAMME", "SAMME.R"))
     random_state::Any      = nothing
 end

src/models/linear-classifiers.jl

@@ -10,6 +10,7 @@ const LogisticClassifier_ = sklm(:LogisticRegression)
     random_state::Any          = nothing
     solver::String             = "lbfgs"::(_ in ("lbfgs", "newton-cg", "liblinear", "sag", "saga"))
     max_iter::Int              = 100::(_ > 0)
+    #TODO: Remove `multi_class` in v1.7+
     multi_class::String        = "auto"::(_ in ("ovr", "multinomial", "auto"))
     verbose::Int               = 0
     warm_start::Bool           = false
@@ -157,6 +158,7 @@ const RidgeCVClassifier_ = sklm(:RidgeClassifierCV)
     scoring::Any          = nothing
     cv::Int               = 5
     class_weight::Any     = nothing
+    #TODO: Use `store_cv_results` in v1.6+
     store_cv_values::Bool = false
 end
 MMI.fitted_params(m::RidgeCVClassifier, (f, _, _)) = (

src/models/linear-regressors.jl

@@ -406,6 +406,7 @@ const RidgeCVRegressor_ = sklm(:RidgeCV)
     scoring::Any             = nothing
     cv::Any                  = 5
     gcv_mode::Option{String} = nothing::(_ === nothing || _ in ("auto","svd","eigen"))
+    #TODO: Use `store_cv_results` in v1.6+
     store_cv_values::Bool    = false
 end
 MMI.fitted_params(model::RidgeCVRegressor, (fitresult, _, _)) = (