Talk:Decision stump

From Wikipedia, the free encyclopedia
Jump to: navigation, search
WikiProject Computing (Rated Stub-class)
WikiProject icon This article is within the scope of WikiProject Computing, a collaborative effort to improve the coverage of computers, computing, and information technology on Wikipedia. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks.
Stub-Class article Stub  This article has been rated as Stub-Class on the project's quality scale.
 ???  This article has not yet received a rating on the project's importance scale.
Note icon
This article has been automatically rated by a bot or other tool as Stub-Class because it uses a stub template. Please ensure the assessment is correct before removing the |auto= parameter.

Can somebody just say what it does really simply? I don't know what a "single level decision tree" is, my brain ain't that fancy. Is a decision stump just thresholding on one of the components of a vector or something?Singularitarian (talk) 00:11, 21 April 2010 (UTC)

In case of continuous or binary features - it compares value of some feature with a threshold, and outputs one or another value depending on the outcome. I.e. in C code that would be "x[i] < threshold ? value1 : value2", where array x[] contains feature values. -- X7q (talk) 06:45, 21 April 2010 (UTC)

Can someone also explain how decision stump finds the best threshold for each feature? Is there any reference for this classifier other than the Weka source document? — Preceding unsigned comment added by (talk) 04:20, 28 March 2012 (UTC)

Well you can simply try all of them and pick the one which minimizes your loss function. There's a finite and rather small number of possible thresholds that lead to different classifications of the training data. Namely try drawing a line between two adjacent points of the training set in each dimension. -- X7q (talk) 23:28, 28 March 2012 (UTC)