  For a solar-like star, the surface rotation evolves with time, allowing in
principle to estimate the age of a star from its surface rotation period. Here
we are interested in measuring surface rotation periods of solar-like stars
observed by the NASA Kepler mission. Different methods have been developed to
track rotation signals in Kepler photometric light curves: time-frequency
analysis based on wavelet techniques, autocorrelation and composite spectrum.
We use the learning abilities of random forest classifiers to take decisions
during two crucial steps of the analysis. First, given some input parameters,
we discriminate the considered Kepler targets between rotating MS stars,
non-rotating MS stars, red giants, binaries and pulsators. We then use a second
classifier only on the MS rotating targets to decide the best data-analysis
treatment.
