In real life applications, we often face the following risk clearance problem: given a set of instances with a known numeric outcome Y (e.g., a toxicity level), we want to learn a model to identify new instances that have a low risk, i.e., the probability of the Y value exceeding a certain maximum M AX is less than some threshold t. This problem guarantees that the cleared instances have the minimum precision of 1 − t for Y ≤ M AX. By clearing such low risk instances, we can allocate costly resources to the remaining high risk instances. In this work, we formulate this problem as Risk Clearance with a goal of maximizing the clearance of low risk instances. Existing classification models fail to solve Risk Clearance adequately, so we develop algorithms designed specifically for this problem. We then validate that our approach improves on existing work via experiments on an industrial case study.