view
View classification tree
Syntax
view(tree)
view(tree,Name,Value)
Description
view( returns
a text description of tree)tree, a decision tree.
view( describes tree,Name,Value)tree with
additional options specified by one or more Name,Value pair
arguments.
Input Arguments
|
A classification tree or compact classification tree created
by |
Name-Value Pair Arguments
Specify optional
comma-separated pairs of Name,Value arguments. Name is
the argument name and Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN.
|
Value describing the display of Default: |
Examples
View Trained Classification Tree
View textual and graphical displays of a trained classification tree.
Load Fisher's iris data set.
load fisheririsTrain a classification tree using all measurements.
Mdl = fitctree(meas,species);
View textual display of the trained classification tree.
view(Mdl)
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
View graphical display of the trained classification tree.
view(Mdl,'Mode','graph');
View Tree from Bag of Trees
Load Fisher's iris data set.
load fisheririsGrow a bag of 100 classification trees using all measurements.
rng(1) % For reproducibility
Mdl = TreeBagger(100,meas,species);Alternatively, you can use fitcensemble to grow a bag of classification trees.
Mdl is a TreeBagger model object. Mdl.Trees stores the bag of 100 trained classification trees in a 100-by-1 cell array. That is, each cell in Mdl.Trees contains a CompactClassificationTree model object.
View a graph of the 10th classification tree in the bag.
Tree10 = Mdl.Trees{10};
view(Tree10,'Mode','graph');
By default, the software grows deep trees for bags of trees.
View Tree from Boosted Ensemble
Load Fisher's iris data set.
load fisheririsBoost an ensemble of 100 classification trees using all measurements. Specify tree stumps as the weak learners.
t = templateTree('MaxNumSplits',1); Mdl = fitcensemble(meas,species,'Method','AdaBoostM2','Learners',t);
Mdl is a ClassificationEnsemble model object. Mdl.Trained stores the ensemble of 100 trained classification trees in a 100-by-1 cell array. That is, each cell in Mdl.Trained contains a CompactClassificationTree model object.
View a graph of the 10th classification tree in the ensemble.
Tree10 = Mdl.Trained{10};
view(Tree10,'Mode','graph');
The graph shows a tree stump because you specified stumps as the weak learners for the ensemble. However, this behavior is not the default for fitcensemble. By default, fitcensemble grows shallow trees for boosted ensembles of trees. That is, 'Learners' is templateTree('MaxNumSplits',10).
Tips
To view tree t from an ensemble of trees, enter one of these lines
of code
view(Ens.Trained{t})
view(Bag.Trees{t})Ensis a full ensemble returned byfitcensembleor a compact ensemble returned bycompact.Bagis a full bag of trees returned byTreeBaggeror a compact bag of trees returned bycompact.
To save tree in the Command Window, get a
figure handle by using the findall and setdiff functions, and then save tree using the function
saveas.
before = findall(groot,'Type','figure'); % Find all figures view(Mdl,'Mode','graph') after = findall(groot,'Type','figure'); h = setdiff(after,before); % Get the figure handle of the tree viewer saveas(h,'a.png')