  Among the population of known galactic black hole X-ray binaries, GRS
1915+105 stands out in multiple ways. It has been in continuous outburst since
1992, and has shown a wide range of different states that can be distinguished
by their timing and spectral properties. These states, also observed in IGR
J17091-3624, have in the past been linked to accretion dynamics. Here, we
present the first comprehensive study into the long-term evolution of GRS
1915+105, using the entire data set observed with RXTE over its sixteen-year
lifetime. We develop a set of descriptive features allowing for automatic
separation of states, and show that supervised machine learning in the form of
logistic regression and random forests can be used to efficiently classify the
entire data set. For the first time, we explore the duty cycle and time
evolution of states over the entire sixteen-year time span, and find that the
temporal distribution of states has significantly changed over the span of the
observations. We connect the machine classification with physical
interpretations of the phenomenology in terms of chaotic and stochastic
processes.
