Work out active learning sample budget instantly with clear inputs, formula shown and shareable results.
Active learning spends a labelling budget in rounds, retraining after each batch so the next query targets the most informative examples. The budget is the seed set plus batch size times rounds, and coverage of the pool is usually only a few percent — which is the entire point, since the gain comes from labelling the uncertain few rather than the redundant many.
Budget
queried = min(pool, batch x rounds); total labels = seed + queried; coverage = queried / pool
Large enough that retraining cost is amortised, small enough that the model actually improves between rounds. One to two percent of the current training set per round is a common starting point.
Uncertainty sampling requires a model. Without a few hundred to a few thousand random labels first, the initial model is too poor to rank informativeness usefully.