MLJ for XGBoost - extracting feature gain

General Usage
2

@Ivan Thanks for reporting this.

In the last breaking release of MLJXGBoostInterface those particular access points were indeed removed. However, MLJ now has a generic feature_importance accessor function you can call on machines wrapping supported models, and the MLJXGBoostInterface models are now supported.

Unfortunately, I just discovered a minor bug, so that only the classifier is currently working. Here’s the workflow in that case:

using MLJ
XGBoostClassifier = @load XGBoostClassifier pkg=XGBoost
X, y = @load_iris

model = XGBoostClassifier()
mach = machine(model, X, y) |> fit!

julia> feature_importances(mach)
4-element Vector{Pair{Symbol, Float32}}:
 :petal_length => 2.991818
  :petal_width => 1.3149351
  :sepal_width => 0.072732545
 :sepal_length => 0.042442977
1 Like