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So it sounds like you’d need your 4 lines of pseudo-code from your first post and then another few lines of comments to describe how your 4 lines replace the au
by abduhl 4y ago
So it sounds like you’d need your 4 lines of pseudo-code from your first post and then another few lines of comments to describe how your 4 lines replace the authors’ publication? Something like “All tree models are partitions of the space based on training data, and the extension to any particular tree model is an obvious exercise left to the reader”?
- JoshCole 4y agoYou are saying it sounds as if he his explicit denial is what he was saying? You're like a kid that hears another kid getting upset at a mispronunciation of their name. "My name is John," he whines. "Sounds like you are Joonnna," you taunt. It is an exceedingly childish thing to do. Maybe others here aren't mature enough to realize you are being a bully, but I'm not stupid enough to think that "not X" said with emotional force means something "sounds like X". I'd also rather give this response rather than making spyware go through the humiliation of repeating himself. He got downvoted because he cussed, not because intentionally distorting what people say is funny.
- spywaregorilla 4y agoWhy have 2 separate people asserted I have issue with the author's publication? My point is that tree based models are models that partition the space. This is not obvious. But the concept of partitioning the space is a very obvious thing to try because it just makes visual sense. Take a look at the example linked above: https://scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_001.png https://scikit-learn.org/stable/_images/sphx_glr_plot_classi... All models indirectly partition the space. Tree based models partition the space explicitly. Other model types do so implicitly.
- JoshCole 4y agoPatience will be necessary; a bad reading of your meaning will be a bit sticky because it allows others to pick up computation where others left off. In the short term it looks like misunderstanding. Over time it means a great deal more thoughtfulness than any one person would have been able to give.