  With the arrival of a number of wide-field snapshot image-plane radio
transient surveys, there will be a huge influx of images in the coming years
making it impossible to manually analyse the datasets. Automated pipelines to
process the information stored in the images are being developed, such as the
LOFAR Transients Pipeline, outputting light curves and various transient
parameters. These pipelines have a number of tuneable parameters that require
training to meet the survey requirements. This paper utilises both observed and
simulated datasets to demonstrate different machine learning strategies that
can be used to train these parameters. The datasets used are from LOFAR
observations and we process the data using the LOFAR Transients Pipeline;
however the strategies developed are applicable to any light curve datasets at
different frequencies and can be adapted to different automated pipelines.
These machine learning strategies are publicly available as Python tools that
can be downloaded and adapted to different datasets
(https://github.com/AntoniaR/TraP_ML_tools).
