MATLAB: How to understand “Decision tree for classification (text description)”

decision tree

The "Decision tree for classification (text description)" is difficult to understand. Who can give detail explanation.

Thank you

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The philosophy of operation of any algorithm based on decision trees is quite simple. In fact, although sometimes containing important differences in the way to do this or that step, any algorithm of this category is based on the strategy of divide and conquer. In general, this philosophy is based on the successive division of the problem into several subproblems with a smaller number of dimensions, until a solution for each of the simpler problems can be found. Based on this principle, the classifiers based on decision trees try to find ways to divide the universe into successively more subgroups (creating nodes containing the respective tests) until each addressing only one class or until one of the classes shows a clear majority do not justifying further divisions, generating in this situation a leaf containing the class majority....

more details here,

http://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Classification/Decision_Trees

http://ocw.mit.edu/courses/sloan-school-of-management/15-097-prediction-machine-learning-and-statistics-spring-2012/lecture-notes/MIT15_097S12_lec08.pdf

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MATLAB: Decision trees, only binary branches

If you fit a classification tree to the famous Fisher iris data, you get this:

>> load fisheriris
>> f = fitctree(meas,species);
>> view(f)
Decision tree for classification
1  if x3<2.45 then node 2 elseif x3>=2.45 then node 3 else setosa
2  class = setosa
3  if x4<1.75 then node 4 elseif x4>=1.75 then node 5 else versicolor
4  if x3<4.95 then node 6 elseif x3>=4.95 then node 7 else versicolor
5  class = virginica
6  if x4<1.65 then node 8 elseif x4>=1.65 then node 9 else versicolor
7  class = virginica
8  class = versicolor
9  class = virginica

So you can see:

The tree has more than two branches
It can predict more than two classes (three in this case)
However, at each branch it makes a binary decision, X<s or X>=s

If X happens to be a categorical predictor, then it still makes a binary decision of the form "X is among this group of categories" or "X is among this other group."

MATLAB: Can someone tell me about the algorithm used to obtain the prediction of the bagged models in Ensemble bagged tree classification in Matlab

In general ensemble classifiers combine a different variety of weak classifiers to reduce high variance. So in general the weak leraners could be any classification models, from logistic regression to nueral nets and could be using any optimization techniques to reduce loss. But in an Ensemble Bagged tree ( I think you are talking about this https://www.mathworks.com/help/stats/treebagger-class.html ) classifier the models would be weak decision trees in which a bootstrap data is drawn for each tree. Decision trees ususally use gini index or entrophy to decide their next branch. Adaboost can also be applied to any ensemble classifier. So in an Ensemble Bagged tree classification all models would be decision trees with varying data due to bagging.

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MATLAB: Decision trees, only binary branches

MATLAB: Can someone tell me about the algorithm used to obtain the prediction of the bagged models in Ensemble bagged tree classification in Matlab

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