  To take unit commitment (UC) decisions under uncertain net load, most studies
utilize a stochastic UC (SUC) model that adopts a one-size-fits-all
representation of uncertainty. Disregarding contextual information such as
weather forecasts and temporal information, these models are typically plagued
by a poor out-of-sample performance. To effectively exploit contextual
information, in this paper, we formulate a conditional SUC problem that is
solved given a covariate observation. The presented problem relies on the true
conditional distribution of net load and so cannot be solved in practice. To
approximate its solution, we put forward a predictive prescription framework,
which leverages a machine learning model to derive weights that are used in
solving a reweighted sample average approximation problem. In contrast with
existing predictive prescription frameworks, we manipulate the weights that the
learning model delivers based on the specific dataset, present a method to
select pertinent covariates, and tune the hyperparameters of the framework
based on the out-of-sample cost of its policies. We conduct extensive numerical
studies, which lay out the relative merits of the framework vis-\`a-vis various
benchmarks.
