I'm trying to build a binary 1/0 ML classification algorithm, and was thinking about how to set up the input dataset. If the event I want to predict (the 1's) occur relatively less frequently in the total data than the 0's, does it makes sense to pare the dataset in such a way to get a more equal distribution of 1's and 0's? Would that be falsely representing the data to the algorithm? What are the disadvantages to doing so?
Define an accuracy metric that reasonably models how you want your algorithm to perform.
Once you have a metric in hand you can cross-validate this question and see if it is an improves the performance.
Some common accuracy metrics that model the problem different: Normalized mutual information, Gini on the labels argsorted by the probabilities, Precision, Recall, AUC.
If the classes are extremely unbalanced and FN are crucial, you'll see an improvement.