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We apply a machine learning algorithm called XGBoost to explore theperiodicity of two radio sources: PKS~1921-293 (OV~236) and PKS~2200+420(BL~Lac), both radio frequency dataset obtained from University of MichiganRadio Astronomy Observatory (UMRAO), at 4.8 GHz, 8.0 GHz, and 14.5 GHz, between1969 to 2012. From this methods, we find that the XGBoost provides theopportunity to use a machine learning based methodology on radio dataset and toextract information with strategies quite different from those traditionallyused to treat time series and to obtain periodicity through the classificationof recurrent events. The results were compared with other methods from othersworks that examined the same dataset and exhibit good agreement with them.

Periodicity detection in AGN with the boosted tree method