  We present a new method employing machine learning techniques for measuring
astrophysical features by correcting systematics in IRAC high precision
photometry using Random Forests. The main systematic in IRAC light curve data
is position changes due to unavoidable telescope motions coupled with an
intrapixel response function. We aim to use the large amount of publicly
available calibration data for the single pixel used for this type of work (the
sweet spot pixel) to make a fast, easy to use, accurate correction to science
data. This correction on calibration data has the advantage of using an
independent dataset instead of using the science data on itself, which has the
disadvantage of including astrophysical variations. After focusing on feature
engineering and hyperparameter optimization, we show that a boosted random
forest model can reduce the data such that we measure the median of ten
archival eclipse observations of XO-3b to be 1459 +- 200 parts per million.
This is a comparable depth to the average of those in the literature done by
seven different methods, however the spread in measurements is 30-100% larger
than those literature values, depending on the reduction method. We also
caution others attempting similar methods to check their results with the
fiducial dataset of XO-3b as we were also able to find models providing
initially great scores on their internal test datasets but whose results
significantly underestimated the eclipse depth of that planet.
