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[D] Random Forests and Decision Trees

I am doing a binary classification problem where I currently run a decision tree across the data with 100 different random seeds, and then take the total number of outputs and figure out the final predicted classification. So if it comes out 1 75 times and 0 25 times, then the final prediction is a 1. I am using a pure majority problem (in the event of a tie, I go with 0). Would there be any benefit to running the exact same thing, but with 100 different random forests? In other words, will a decision tree and random forest predict the same wrong ones, but predict different correct ones? I am trying to find a way to push the accuracy a little higher. It works well, coming in with about 65% accuracy.

P.S. I do all the normal stuff like train-test split, limit the number of branches to the decision tree, etc.

P.P.S. I should note that the random seed changes for the train-test split and the decision tree when running the next tree.

submitted by /u/spot4992
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.