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I've halted this line of investigation for now, but hope to return to it later.
Because we construct features by using examples other than the one that we're trying to make a prediction about, splitting up an event between train and test sets gives rise to a situation where the train set is's contaminated with the variable that we're trying to predict, so random forest models end up being a poor choice. I initially used hundreds of randomly generated train/test splits for cross validation, and later realized that doing doesn't add clarity beyond that offered by the simpler method of doing cross validation at the level of individual events.
Visuals The folder contains some visualizations that are helpful in understanding the dataset, but is currently very incomplete.
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