The creation of a labelled dataset for machine learning purposes is a costly process. In recent works, it has been shown that a mix of crowdsourcing and active learning approaches can be used to annotate objects at an affordable cost. In this paper, we study the gamification of machine learning techniques; in particular, the problem of classification of objects. In this first pilot study, we designed a simple game, based on a visual interpretation of probabilistic classifiers, that consists in separating two sets of coloured points on a two-dimensional plane by means of a straight line. We present the current results of this first experiment that we used to collect the requirements for the next version of the game and to analyze i) what is the 'price' to build a reasonably accurate classifier with a small amount of labelled objects, ii) and compare the accuracy of the player to the state-of-the-art classification algorithms.