3 ms·
If your boundaries are so easily visualized that you can draw a circle around them, then why not use a fully automatic(tm) decision tree, and let the computer d
by refactor_master 6y ago
If your boundaries are so easily visualized that you can draw a circle around them, then why not use a fully automatic(tm) decision tree, and let the computer do the hard work?
This is like the fits people drew on paper before computers were available.
- Der_Einzige 6y agohumans provide accidental regularization - and a human made decision boundary can have further fine-tuning by an expert. This is important related to things like calibrating probabilities, which decision trees do very poorly on even if they get good scores on the metric they're trained on. Even better if you have a generative model which can "hallucinate" parts of the decision boundary which don't have points. Now you can "probe" your model and have a human intervene if they think parts of the decision boundary are wrong I suppose a human can modify a decision tree as well - but in practice human generated rules may be better for some domains.