Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Comparing interpretability and explainability for feature selection   A common approach for feature selection is to examine the variable importance
scores for a machine learning model, as a way to understand which features are
the most relevant for making predictions. Given the significance of feature
selection, it is crucial for the calculated importance scores to reflect
reality. Falsely overestimating the importance of irrelevant features can lead
to false discoveries, while underestimating importance of relevant features may
lead us to discard important features, resulting in poor model performance.
Additionally, black-box models like XGBoost provide state-of-the art predictive
performance, but cannot be easily understood by humans, and thus we rely on
variable importance scores or methods for explainability like SHAP to offer
insight into their behavior.
  In this paper, we investigate the performance of variable importance as a
feature selection method across various black-box and interpretable machine
learning methods. We compare the ability of CART, Optimal Trees, XGBoost and
SHAP to correctly identify the relevant subset of variables across a number of
experiments. The results show that regardless of whether we use the native
variable importance method or SHAP, XGBoost fails to clearly distinguish
between relevant and irrelevant features. On the other hand, the interpretable
methods are able to correctly and efficiently identify irrelevant features, and
thus offer significantly better performance for feature selection.

3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom