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[D] Tips on improving random forest predictive accuracy when # of features is really low?

Working on a random forest predictive model with a continuous response variable and two continuous features. Normally when I do RF projects I use some sort of feature selection method to choose which features to use. Then I fit the RF model onto those features. Then to test accuracy / related metrics I use cross validation, confusion matrices, etc.

However in this case I only have two given features. I don’t want to just literally run a RF model on those two features as my whole entire project. I’m thinking gradient boosting is what I should learn? Also I think I should play around with the number of estimators and depth of the RF. I’m using sklearn in Python if that helps.

Any other suggestions? Obviously this type of problem/challenge is an unexplored area for me, so looking for best practices on how to add to my data science toolkit. Thanks!

submitted by /u/truryce
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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.