  Feature selection is a standard approach to understanding and modeling
high-dimensional classification data, but the corresponding statistical methods
hinge on tuning parameters that are difficult to calibrate. In particular,
existing calibration schemes in the logistic regression framework lack any
finite sample guarantees. In this paper, we introduce a novel calibration
scheme for $\ell_1$-penalized logistic regression. It is based on simple tests
along the tuning parameter path and is equipped with optimal guarantees for
feature selection. It is also amenable to easy and efficient implementations,
and it rivals or outmatches existing methods in simulations and real data
applications.
