<p>Strategic classification is the problem of classifying feature vectors that can be manipulated at the testing phase of the classifier. The classifier aims for its decision rule to be robust against strategic manipulation and also be efficiently learnable. In this paper, we present two main ideas for the classifier to achieve this goal. The first idea is to enrich the classifier’s decision-making capabilities in two ways: ignoring feature vectors and making randomized decisions. This approach helps the classifier classify more feature vectors correctly by limiting the sender’s options for manipulation. The second idea is to use the strategic structure of the problem during learning. This approach, in certain contexts, can simplify the learning process. Combining these ideas, we propose and compare two learning algorithms, Vanilla ERM and Strategy-aware ERM, and provide a heuristic for selecting the one that yields a classifier both robust to strategic manipulation and efficiently learnable.</p>