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We present a new method employing machine learning techniques for measuringastrophysical features by correcting systematics in IRAC high precisionphotometry using Random Forests. The main systematic in IRAC light curve datais position changes due to unavoidable telescope motions coupled with anintrapixel response function. We aim to use the large amount of publiclyavailable calibration data for the single pixel used for this type of work (thesweet spot pixel) to make a fast, easy to use, accurate correction to sciencedata. This correction on calibration data has the advantage of using anindependent dataset instead of using the science data on itself, which has thedisadvantage of including astrophysical variations. After focusing on featureengineering and hyperparameter optimization, we show that a boosted randomforest model can reduce the data such that we measure the median of tenarchival 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 byseven different methods, however the spread in measurements is 30-100% largerthan those literature values, depending on the reduction method. We alsocaution others attempting similar methods to check their results with thefiducial dataset of XO-3b as we were also able to find models providinginitially great scores on their internal test datasets but whose resultssignificantly underestimated the eclipse depth of that planet.

Random Forests applied to High Precision Photometry Analysis with Spitzer IRAC