In active learning, a query strategy is the method by which the algorithm chooses the next data points to query for labels. Rather than randomly labeling data, the model “asks questions” in the form of selecting unlabeled instances it finds most informative for improving its accuracy. The goal is to maximize model learning while minimizing the number of labeled examples required.​Common query strategies include:Uncertainty sampling: select samples where the model is least certain of the prediction.Query-by-committee: use an ensemble of models and select samples where they disagree most.Diversity-based methods: select a set of points that are representative of the remaining pool.By employing a clever query strategy, active learning focuses labeling effort on the most valuable data​, improving efficiency.