  Feature selection in machine learning is subject to the intrinsic randomness
of the feature selection algorithms (for example, random permutations during
MDA). Stability of selected features with respect to such randomness is
essential to the human interpretability of a machine learning algorithm. We
proposes a rank based stability metric called instability index to compare the
stabilities of three feature selection algorithms MDA, LIME, and SHAP as
applied to random forests. Typically, features are selected by averaging many
random iterations of a selection algorithm. Though we find that the variability
of the selected features does decrease as the number of iterations increases,
it does not go to zero, and the features selected by the three algorithms do
not necessarily converge to the same set. We find LIME and SHAP to be more
stable than MDA, and LIME is at least as stable as SHAP for the top ranked
features. Hence overall LIME is best suited for human interpretability.
However, the selected set of features from all three algorithms significantly
improves various predictive metrics out of sample, and their predictive
performances do not differ significantly. Experiments were conducted on
synthetic datasets, two public benchmark datasets, and on proprietary data from
an active investment strategy.

Machine Learning;Computational Finance;Machine Learning