EDIT: Turned out to be a mistake in my normalization.I'm building a model that can differentiate between -- let's say -- peanuts and chickpeas.I have a dataset that measures weight and density, which a random forest model could classify with ~90% accuracy. I created box and whisker plots of the features to confirm that there seem to be significant differences between the two groups' weight and density. The risk of overfitting should be low.I scraped together some more data -- let's say fat content. I made another box and whisker and, sure enough, chickpeas and peanuts have significantly different fat contents.But I reran the random forest with all three factors, and performance went down! To ~80%.I'm still digging for something stupid in my code. But if these results are accurate, what do you think? Shouldn't more, differentiating data improve results?
New, Seemingly-High Quality Data Leading to Worse Random Forest Results?
feature selection;random forest