API Reference

API Reference

Documentation for the OptimalFeatureSelection public interface.

Index

Types

Abstract type encompassing all optimal feature selection learners.

Learner for conducting optimal feature selection on classification problems.

The following parameters are supported (refer to the documentation for each):

Learner for conducting optimal feature selection on regression problems.

The following parameters are supported (refer to the documentation for each):

Model Details

These functions can be used to query the structure of a OptimalFeatureSelectionLearner. The examples make use of the following learner:

Fitted OptimalFeatureSelectionClassifier:
  Constant: -3.34926
  Weights:
    region==C:  2.72815
    score2:     0.0558302
get_prediction_constant(lnr::OptimalFeatureSelectionLearner)

Return the constant term in the prediction in the trained lnr.

Example

IAI.get_prediction_constant(lnr)
-3.3492558131278978
get_prediction_weights(lnr::OptimalFeatureSelectionLearner)

Return the weights for each feature in the prediction in the trained lnr. The weights are returned as two Dicts, one for numeric features and one for categoric features.

The numeric Dict has key-value pairs of feature names and their corresponding weights in the prediction.

The categoric Dict has key-value pairs of feature names and a corresponding Dict that maps the categoric levels for that feature to their weights in the prediction.

Any feature not included in either Dict has zero weight in the prediction, and similarly, any categoric levels that are not included have zero weight.

Example

numeric_weights, categoric_weights = IAI.get_prediction_weights(lnr)
numeric_weights
Dict{Symbol,Float64} with 1 entry:
  :score2 => 0.0558302
categoric_weights
Dict{Symbol,Dict{Any,Float64}} with 1 entry:
  :region => Dict{Any,Float64}("C"=>2.72815)