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I don't think random forests are "magical" at all. I think you can walk a non-technical audience from decision trees to bagged and boosted forests on a whiteboa
by cwyers 7y ago
I don't think random forests are "magical" at all. I think you can walk a non-technical audience from decision trees to bagged and boosted forests on a whiteboard in under an hour. (I don't think you can do this nearly as well for maximum likelihood estimation, honestly.) But there's still a difference between a mathematical function that you're trying to find the optimal values for and something that's defined by an algorithm that there's no single function to optimize.
- taeric 7y agoBut, at the end of the day, you can get some easy to understand statistics from building a decision tree. Based on population makeup, you'll know solid numbers on how many will be expected to hit each outcome. Such that optimizing some other function based on the application of a decision tree is effectively a statistical question. A natural one. Indeed, I'm not sure I see the difference. One is just explainable based on understandings of distributions and trusting that they hold. One is explainable based on understanding of decisions, and trusting those remain the best decisions. I used my bike ride as an example earlier. It is made up of many decisions to get home. Knowing what all of those decisions are can help build up a solid range of when I'm likely to get home. For the level of accuracy I typically care about, so does just knowing the rough distribution of how long I typically take. How are both of those not statistical in nature? One is just more fully exploring a space with ridiculous computational power, whereas the other is generalizing to a much quicker answer. Right?