Talk:Decision tree learning

message: 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)
thread_title: Untitled

message: 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)
replies: text: Agree - the pile of superfluous citations is entirely unneccessary. Removed. --mcld (talk) 12:03, 3 November 2009 (UTC)
thread_title:  References 

message: 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)
thread_title:  Poor explanation 

message: I'm surprised that there is no information about Bayesian CART. It is highly used in many models now. Somebody should add information about this on the page.
Bayesian CART Model Search by. Hugh Chipman, Edward I. George and Robert E. McCulloch
This is the paper talking about the Bayesian method to produce trees. This paper has been cited 298 times. Many modern tree models use it as a basis for creating the trees.
74.56.25.108 (talk) 23:37, 25 September 2011 (UTC)
thread_title:  Bayesian CART 

message: Rather than creating rich context, using Titanic survival as an example invites needless (here) political and social distractions, and should be replaced with a less complex example.  The existing example distracts the reader with questions of the role of women in the early 20th century and why wealth (class of accommodation) was omitted by the example writer.  Also, the decisions based on .5 values of integers (years of age; number of siblings+spouses) are needlessly confusing.  I'm not an expert in decision trees, and so appeal to those more familiar with the topic to offer a better Decision Tree example.
Knowthhill (talk) 17:04, 14 April 2013 (UTC)
replies: text: I do agree, there are no citations for the values quoted as well. A different cited example would be good. --Salix (talk): 19:09, 14 April 2013 (UTC)
thread_title:  Poor example tree 

message: There should be a section talking about what decision trees are used for out in the wild.Richard☺Decal (talk) 20:08, 24 March 2017 (UTC)
thread_title:  'Uses' section 

message: This seems to be a very damning, yet very controversial statement, thus it should either have more support and explanation, be re-written, or be removed.
One textbook is referenced to which I do not have access. OTOH I could reference several hundred scholarly articles indicating the opposite, including famous ones such as Breiman's*** (and to be fair, I could also add some references from articles that may provide additional support to the original statement). 
I don't want to start an editing war, but would it be fair to mitigate this statement?
Note that the author doesn't even tell us what the other, allegedly more accurate, methods are and/or under what circumstances accuracy is higher for the other algorithms and worse for tree models.
*** Just a couple I quickly grabbed as examples:
Breiman, L. Machine Learning (2001) 45: 5. doi:10.1023/A:1010933404324
Breiman. L. (1998b). Randomizing outputs to increase prediction accuracy. Technical Report 518, May 1, 1998, Statistics Department, UCB (in press, Machine Learning).
96.244.43.227 (talk) 12:50, 27 May 2017 (UTC)
thread_title:  Questionable statement in opening line of Limitations: "Trees do not tend to be as accurate as other approaches." 

message: This article has been targeted by an (apparent) campaign to insert "Decision Stream" into various Wikipedia pages about Machine Learning. "Decision Stream" refers to a recently published paper that currently has zero academic citations.  The number of articles that have been specifically edited to include "Decision Stream" within the last couple of months suggests  conflict-of-interest editing by someone who wants to advertise this paper.  They are monitoring these pages and quickly reverting any edits to remove this content.
Known articles targeted:
* Artificial intelligence
* Statistical classification
* Deep learning
* Random forest
* Decision tree learning
* Decision tree
* Pruning (decision trees)
* Predictive analytics
* Chi-square automatic interaction detection 
* MNIST database   — Preceding unsigned comment added by ForgotMyPW (talk • contribs) 17:39, 2 September 2018 (UTC)
thread_title:  "Decision Stream" Editing Campaign 

message: I understand the need for [citation needed] when claims are made without citation, but this sentence:
A tree can be "learned"[clarification needed] by splitting the source set[clarification needed] into subsets based on an attribute value test[clarification needed][citation needed].
Is near unreadable. Wouldn't a single notation been sufficient?  — Preceding unsigned comment added by 198.244.99.210 (talk) 06:48, 23 November 2018 (UTC)
thread_title:  Aggressive [citation/citation needed] 

message: The split should be minimization of overall sum of squares and not the reduction in variance (c.f, Hastie,Tibshirani & Friedman, Elements of Statistical Learning). Specifically, the criteria ought to be 
 \sum_{ i \neq j \in S} (x_i -x_j)^2 - (\sum_{ i \neq j \in S_l} (x_i -x_j)^2 +\sum_{ i \neq j \in S_r} (x_i -x_j)^2 ) 
where  S, S_l \text{and} S_r  represents the set of points at the node, the left and right leaf nodes.  — Preceding unsigned comment added by 115.110.204.60 (talk) 06:19, 25 February 2019 (UTC)
replies: text: Yup! This has been fixed. Bennetto (talk) 22:31, 10 March 2021 (UTC)
thread_title:  Split criteria for regression trees 

message: The information gain example reads "To find the information gain of the split using windy, we must first calculate the information in the data before the split." but the sentence is near the end of the example. So I suggest to put it at the beginning, which would be more consistent with the equation before the  example.  — Preceding unsigned comment added by Vl8r (talk • contribs) 14:21, 13 October 2020 (UTC)
thread_title:  Order of the calculus in the Information gain example 

message: In Metrics > Information gain, there attached an example:
"Consider an example data set with four attributes: outlook (sunny, overcast, rainy), temperature (hot, mild, cool), humidity (high, normal), and windy (true, false), with a binary (yes or no) target variable, play, and 14 data points. To construct a decision tree on this data, we need to compare the information gain of each of the four trees, each split on one of the four features. The split with the highest information gain will be taken as the first split and the process will continue until all children nodes each have consistent data, or until the information gain is 0.
To find the information gain of the split using windy, we must first calculate the information in the data before the split. The original data contained nine yes's and five no's."
At the end of the subsection "Information gain", the article did quote: "This example is adapted from the example appearing in Witten et al.". 
However, to speak of the dataset (and perform mathematics on said dataset) without giving examples of the dataset (ie. how the dataset looks, what it is comprised of) yields little demonstrative values.
The writer of this article needs to attach the dataset before the example. 2402:800:61B1:7C49:B1AD:31EB:F1EC:29CF (talk) 04:29, 3 February 2023 (UTC)
thread_title:  Needs to quote dataset in Metrics > Information gain