Interest has been growing within HCI on the use of machine learning and reasoning in applications to classify such hidden states as user intentions, based on observations. HCI researchers with these interests typically have little expertise in machine learning and often employ toolkits as relatively fixed “black boxes” for generating statistical classifiers. However, attempts to tailor the performance of classifiers to specific application requirements may require a more sophisticated understanding and custom-tailoring of methods. We present ManiMatrix, a system that provides controls and visualizations that enable system builders to refine the behavior of classification systems in an intuitive manner. With ManiMatrix, users directly refine parameters of a confusion matrix via an interactive cycle of re-classification and visualization. We present the core methods and evaluate the effectiveness of the approach in a user study. Results show that users are able to quickly and effectively modify decision boundaries of classifiers to tailor the behavior of classifiers to problems at hand.