Выбор модели

julia> using ScikitLearn, Random

julia> Random.seed!(11);

julia> X = rand(25,4); y = rand([true,false], 25);

julia> @sk_import linear_model: LogisticRegression;

julia> using ScikitLearn.GridSearch: GridSearchCV

julia> gridsearch = GridSearchCV(LogisticRegression(max_iter=200), Dict(:C => 0.1:0.1:2.0))
GridSearchCV
  estimator: PyCall.PyObject
  param_grid: Dict{Symbol,StepRangeLen{Float64,Base.TwicePrecision{Float64},Base.TwicePrecision{Float64}}}
  scoring: Nothing nothing
  loss_func: Nothing nothing
  score_func: Nothing nothing
  fit_params: Dict{Any,Any}
  n_jobs: Int64 1
  iid: Bool true
  refit: Bool true
  cv: Nothing nothing
  verbose: Int64 0
  error_score: String "raise"
  scorer_: Nothing nothing
  best_params_: Nothing nothing
  best_score_: Nothing nothing
  grid_scores_: Nothing nothing
  best_estimator_: Nothing nothing



julia> fit!(gridsearch, X, y)
GridSearchCV
  estimator: PyCall.PyObject
  param_grid: Dict{Symbol,StepRangeLen{Float64,Base.TwicePrecision{Float64},Base.TwicePrecision{Float64}}}
  scoring: Nothing nothing
  loss_func: Nothing nothing
  score_func: Nothing nothing
  fit_params: Dict{Any,Any}
  n_jobs: Int64 1
  iid: Bool true
  refit: Bool true
  cv: Nothing nothing
  verbose: Int64 0
  error_score: String "raise"
  scorer_: score (function of type typeof(score))
  best_params_: Dict{Symbol,Any}
  best_score_: Float64 0.6
  grid_scores_: Array{ScikitLearn.Skcore.CVScoreTuple}((20,))
  best_estimator_: PyCall.PyObject



julia> println("Best hyper-parameters: $(gridsearch.best_params_)")
Best hyper-parameters: Dict{Symbol,Any}(:C => 0.8)