  The system operators usually need to solve large-scale unit commitment
problems within limited time frame for computation. This paper provides a
pragmatic solution, showing how by learning and predicting the on/off
commitment decisions of conventional units, there is a potential for system
operators to warm start their solver and speed up their computation
significantly. For the prediction, we train linear and kernelized support
vector machine classifiers, providing an out-of-sample performance guarantee if
properly regularized, converting to distributionally robust classifiers. For
the unit commitment problem, we solve a mixed-integer second-order cone
problem. Our results based on the IEEE 6- and 118-bus test systems show that
the kernelized SVM with proper regularization outperforms other classifiers,
reducing the computational time by a factor of 1.7. In addition, if there is a
tight computational limit, while the unit commitment problem without warm start
is far away from the optimal solution, its warmly-started version can be solved
to (near) optimality within the time limit.
