Engee documentation

Model Selection

Most models contain hyperparameters: parameters that are specified in the constructor, and not learned from the data. ScikitLearn.jl provides GridSearchCV to find the best set of hyper-parameter:

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)

See ?GridSearchCV and the scikit-learn docs for details.

Examples

RandomizedSearchCV will sample from each parameter independently. Documentation here.

The distributions have to be specified using scipy.stats (see example below), but we hope to support Distributions.jl in the future. File an issue if this is a pain point.

Examples