Talk:Decision tree learning

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I'm very grateful for the work that's gone into this article so far, but I think that it's missing a couple of key points that would be helpful to readers. First, there is no discussion of pruning - what necessitates it, and what algorithms are used to guide it? Second, although Gini impurity and Information gain are discussed in their own section, there is no discussion explaining their application in the construction of decision trees (i.e. as criteria for selecting values on which to split a node).

Here's hoping that these observations are not out of place; this is my first-ever contribution to Wikipedia and I suppose I ought to go play in the sandbox and then add to the article myself, eh?

Yoy riblet 04:41, 18 July 2007 (UTC)[reply]

There's also a section missing on the _disadvantages_ of decision trees. For instance, ISTM that a decision tree can only divide the input region with axis-parallel lines (neural networks do not have this restriction). -Thenickdude (talk) 02:20, 3 November 2008 (UTC)[reply]

Copyright problem

I'm concerned that the example in this page is needlessly copied without attribution from Quinlan's paper "Induction of Decision Trees" - in particular the table's content and organization is directly copied from Table 1. This is unlikely to be considered fair use since the specific example is not needed to comment on the method, much less decision trees in general. I suggest creating a novel example for this article (I don't believe Wikipedia:No original research is an issue here, since we're describing decision trees in general and not any particular algorithm). Dcoetzee 00:10, 1 March 2009 (UTC)[reply]

I agree - the example seems to have been lifted from Quinlan, with tweaks. More importantly from a pedagogical standpoint, the example is not about decision trees in machine learning, but about decision trees in decision analysis, so it's in the wrong page. I am removing that section, as I don't see any benefit to having it in this article. --mcld (talk) 11:49, 3 November 2009 (UTC)[reply]

On a related note: the example is weird. The target variable is sort of a pretend categorical variable with values "Play" or "Don't Play", when what is really being measured is the number of people who show up on a given day. The example talks about the decisions as if they are describing groups of people, which is a stretch from the data. If there are no objections, I'm going to make up a completely different example, using Wikipedia data. riedl 12 June 2009

References

With all due respect to the work of Drs. Tan and Dowe -- I doubt that multiple references to their publications are of uttermost importance to an overview article on "Decision tree learning". This might be reviewed. BM 128.31.35.198 (talk) 13:37, 24 April 2009 (UTC)[reply]

Agree - the pile of superfluous citations is entirely unneccessary. Removed. --mcld (talk) 12:03, 3 November 2009 (UTC)[reply]

Poor explanation

I read the general description several times and I'm confused. First of all, what is the target variable in the graph? There are two numbers under each leaf (I'll assume it's the probability of survival). What is the source set? The set of passengers? How do you assign probability of survival to a given passenger? What is the meaning of "... when the subset at a node all has the same value of the target variable"? Passengers with the same probability of survival? The graph will only make sense as an illustration if the mapping between it and the general description is made explicit at every step. Bartosz (talk) 19:46, 10 June 2011 (UTC)[reply]

Inclusion of "QuickDT" in list of Implementations

Two days ago a new decision tree learning implementation called QuickDT was added to the list of implementations. This is after QuickDT was posted to reddit/r/MachineLearning, to a favorable response, and also to mloss.org. It is already in commercial usage, and its author is well known.

Shortly afterwards this was removed by User:X7q with the explanation "wikipedia is not a place to promote your pet projects".

My opinion is that there is as much justification to include QuickDT in this list as any of the implementations already listed, and so I have restored the link.

If User:X7q would like to elaborate on their reason for thinking that this decision tree learning implementation should not be included in this list of decision tree learning implementations, I am happy to discuss it.

--Javalangstring (talk) 15:59, 22 September 2011 (UTC)[reply]