  In this work, we study some novel applications of conformal inference
techniques to the problem of providing machine learning procedures with more
transparent, accurate, and practical performance guarantees. We provide a
natural extension of the traditional conformal prediction framework, done in
such a way that we can make valid and well-calibrated predictive statements
about the future performance of arbitrary learning algorithms, when passed an
as-yet unseen training set. In addition, we include some nascent empirical
examples to illustrate potential applications.
