I have a question about make_classification from scikit-learn. I have created a dataset with make_classification and the aim is to test how well different models can distinguish important features from less important features.

How can I set an experiment in which I can evaluate whether a model is able to identify the variables that have an influence?

I have looked at the documentation of make_classification, but unfortunately I did not get any further.

I have set the following:

X,y = make_classification(n_samples=50000, n_features=10, n_informative=5, 
                    n_redundant=2, n_repeated=0, n_classes=2, n_clusters_per_class=2,
                          class_sep=1,
                   flip_y=0.01, weights=[0.9,0.1], shuffle=True, random_state=42)

Thank you, any ideas or advice are highly appreciated.