  We look at whether machine learning can predict the final objective function
value of a difficult combinatorial optimisation problem from the input. Our
context is the pattern reduction problem, one industrially important but
difficult aspect of the cutting stock problem. Machine learning appears to have
higher prediction accuracy than a na\"ive model, reducing mean absolute
percentage error (MAPE) from 12.0% to 8.7%.
