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'Classification And Regression Trees'. CART is a popular data mining technique.

1
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Any time you have the true/generative model having some sort of curvature in your predictor space (e.g. $x_i^2$ or interactions of the form $x_i\times x_j$) you cannot capture/approximate this behavio …
answered Jul 28 '17 by Lucas Roberts
39
votes
Decision trees algorithms do not compute all possible trees when they fit a tree. If they did they would be solving an NP-hard problem. Decision tree fitting algorithms typically make greedy decisions …
answered Jul 24 '17 by Lucas Roberts
1
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single prediction, each tree gets an equal vote. So in short, the RandomForest algorithm is also greedy in the same sense as the CART algorithm. The RandomForest algorithm has a sample with replacement of … the observations in the data so each tree will be slightly different. Given that CART is a non-stable algorithm (small changes of input data may lead to drastically different trees), this random sampling may alleviate the drawbacks of the greedy fitting of the CART fitting process. …
answered Sep 18 '17 by Lucas Roberts
0
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Lii's answer applies to current terminal nodes. It might be worth noting that this is true if you condition on the current configuration of the splits (in the tree above where you are proposing any sp …
answered Dec 13 '17 by Lucas Roberts
0
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There are several attempts to incorporate alternative variations of splitting rules into decisions trees via generalizing the CART algorithm. One prominent one is GUIDE by Wei-Yin Loh in Wisconsin …
answered Jul 28 '17 by Lucas Roberts
2
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It depends on what algorithm is being used to build the tree. CART trees are invariant to scale changes so a log transform should not change the resulting tree. However, the values of the split rules …
answered Dec 30 '17 by Lucas Roberts
0
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The answer to your question depends on what class of split rules you allow in the fitting of a decision tree. If the only class of allowable splits are on a single variable you will never be able to c …
answered Aug 24 '17 by Lucas Roberts
2
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1answer
What is the intuition behind why randomForest works well with prediction on lots of applied datasets? No fancy math required for an answer-but if you have some that helps intuition great.
asked Dec 28 '17 by Lucas Roberts
0
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The difference between the squared error minimization that is done for each split in a decision tree and that is done for a typical $L_2$ loss optimization has 1 subtle distinction: The decision tre …
answered Aug 24 '17 by Lucas Roberts