package modelkit

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package modelkit
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Module Multiclass_classification.Make

Parameters

Signature

type params = {
  1. min_feature_count : int;
  2. step : Recursive_feature_elimination.step;
  3. max_fits : int option;
  4. scorer_name : string;
  5. estimator_params : Estimator.params;
}
type t
type fitted
val create : ?min_feature_count:int -> ?step:Recursive_feature_elimination.step -> ?max_fits:int -> ?execution:Execution.t -> splitter:Target.classification Target.t Cross_validation.splitter -> scorer:Multiclass_classification_scorer.t -> Estimator.t -> (t, Error.t) result
val cv_results : fitted -> score array
val selected_feature_count : fitted -> int
val selected_indices : fitted -> int array
val ranking : fitted -> int array
val final_importances : fitted -> Vector.t
val fitted_estimator : fitted -> Estimator.fitted
val fit_count : fitted -> int
include METADATA_TRANSFORMER with type t := t and type params := params and type target = Target.classification Target.t and type fitted := fitted and type rng = Rng.t
include SPECIFICATION with type t := t with type params := params
val clone : t -> t
val params : t -> params
type rng = Rng.t
val fit_request : t -> Metadata.Request.t
val transform_request : t -> Metadata.Request.t
val fit : t -> metadata:Metadata.t -> rng:rng -> feature_schema:Feature_schema.t -> x:Matrix.t -> y:target option -> unit -> (fitted, Error.t) result
val transform : fitted -> metadata:Metadata.t -> feature_schema:Feature_schema.t -> x:Matrix.t -> (Matrix.t, Error.t) result
val fitted_params : fitted -> params
val input_schema : fitted -> Feature_schema.t
val output_schema : fitted -> Feature_schema.t