  While Machine learning gives rise to astonishing results in automated
systems, it is usually at the cost of large data requirements. This makes many
successful algorithms from machine learning unsuitable for human-machine
interaction, where the machine must learn from a small number of training
samples that can be provided by a user within a reasonable time frame.
Fortunately, the user can tailor the training data they create to be as useful
as possible, severely limiting its necessary size -- as long as they know about
the machine's requirements and limitations. Of course, acquiring this knowledge
can in turn be cumbersome and costly. This raises the question of how easy
machine learning algorithms are to interact with. In this work, we address this
issue by analyzing the intuitiveness of certain algorithms when they are
actively taught by users. After developing a theoretical framework of
intuitiveness as a property of algorithms, we introduce an active teaching
paradigm involving a prototypical two-dimensional spatial learning task as a
method to judge the efficacy of human-machine interactions. Finally, we present
and discuss the results of a large-scale user study into the performance and
teaching strategies of 800 users interacting with two prominent machine
learning algorithms in our system, providing first evidence for the role of
intuition as an important factor impacting human-machine interaction.
