Documentation

This is machine translation

Translated by Microsoft
Mouse over text to see original. Click the button below to return to the English verison of the page.

Syntax

view(tree)
view(tree,Name,Value)

Description

view(tree) returns a text description of tree, a decision tree.

view(tree,Name,Value) describes tree with additional options specified by one or more Name,Value pair arguments.

Input Arguments

tree

A regression tree or compact regression tree created by fitrtree or compact.

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 single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

'Mode'

Display of tree, either 'graph' or 'text'. 'graph' opens a GUI displaying tree, and containing controls for querying the tree. 'text' sends output to the Command Window describing tree.

Default: 'text'

Tip

To view tree t from an ensemble of trees, enter one of these lines of code

view(Ens.Trained{t})
view(Bag.Trees{t})

  • Ens is a full ensemble returned by fitensemble or a compact ensemble returned by compact.

  • Bag is a full bag of trees returned by TreeBagger or a compact bag of trees returned by compact.

Examples

expand all

View textual and graphical displays of a trained regression tree.

Load the carsmall data set. Consider a model that explains a car's fuel economy (MPG) using its weight (Weight) and number of cylinders (Cylinders).

load carsmall
X = [Weight Cylinders];
Y = MPG;

Train a regression tree using all measurements.

Mdl = fitrtree(X,Y);

View textual display of the trained regression tree.

view(Mdl)
Decision tree for regression
 1  if x1<3085.5 then node 2 elseif x1>=3085.5 then node 3 else 23.7181
 2  if x1<2371 then node 4 elseif x1>=2371 then node 5 else 28.7931
 3  if x2<7 then node 6 elseif x2>=7 then node 7 else 15.5417
 4  if x1<2162 then node 8 elseif x1>=2162 then node 9 else 32.0741
 5  if x2<5 then node 10 elseif x2>=5 then node 11 else 25.9355
 6  fit = 19.2778
 7  if x1<4381 then node 12 elseif x1>=4381 then node 13 else 14.2963
 8  if x1<1951 then node 14 elseif x1>=1951 then node 15 else 33.3056
 9  fit = 29.6111
10  if x1<2827.5 then node 16 elseif x1>=2827.5 then node 17 else 27.2143
11  if x1<3013.5 then node 18 elseif x1>=3013.5 then node 19 else 23.25
12  if x1<3533.5 then node 20 elseif x1>=3533.5 then node 21 else 14.8696
13  fit = 11
14  fit = 29.375
15  if x1<2142.5 then node 22 elseif x1>=2142.5 then node 23 else 34.4286
16  if x1<2385 then node 24 elseif x1>=2385 then node 25 else 27.6389
17  fit = 24.6667
18  fit = 21.5
19  fit = 30.25
20  fit = 16.6
21  if x1<4378 then node 26 elseif x1>=4378 then node 27 else 14.3889
22  if x1<2080 then node 28 elseif x1>=2080 then node 29 else 34.8333
23  fit = 32
24  fit = 24.5
25  if x1<2412.5 then node 30 elseif x1>=2412.5 then node 31 else 28.0313
26  if x1<4365 then node 32 elseif x1>=4365 then node 33 else 14.2647
27  fit = 16.5
28  fit = 34.125
29  fit = 36.25
30  fit = 34
31  if x1<2447 then node 34 elseif x1>=2447 then node 35 else 27.6333
32  if x1<4122.5 then node 36 elseif x1>=4122.5 then node 37 else 14.5313
33  fit = 10
34  fit = 24
35  if x1<2573.5 then node 38 elseif x1>=2573.5 then node 39 else 27.8929
36  if x1<3860 then node 40 elseif x1>=3860 then node 41 else 14.15
37  fit = 15.1667
38  fit = 27.125
39  if x1<2580 then node 42 elseif x1>=2580 then node 43 else 28.2
40  fit = 14.5
41  fit = 13.625
42  fit = 31
43  fit = 27.8889

View graphical display of the trained regression tree.

view(Mdl,'Mode','graph');

Load the carsmall data set. Consider a model that explains a car's fuel economy (MPG) using its weight (Weight) and number of cylinders (Cylinders).

load carsmall
X = [Weight Cylinders];
Y = MPG;

Grow a bag of 100 regression trees using all measurements.

rng(1) % For reproducibility
Mdl = TreeBagger(100,X,Y);

Alternatively, you can use fitensemble to grow a bag of regression trees.

Mdl is a TreeBagger model object. Mdl.Trees stores the bag of 100 trained regression trees in a 100-by-1 cell array. That is, each cell in Mdl.Trees contains a CompactRegressionTree model object.

View a graph of the 10th regression tree in the bag.

Tree10 = Mdl.Trees{10};
view(Tree10,'Mode','graph');

By default, the software grows deep trees for bags of trees.

Load the carsmall data set. Consider a model that explains a car's fuel economy (MPG) using its weight (Weight) and number of cylinders (Cylinders).

load carsmall
X = [Weight Cylinders];
Y = MPG;

Boost an ensemble of 100 regression trees using all measurements.

Mdl = fitensemble(X,Y,'LSBoost',100,'Tree');

Mdl is a RegressionEnsemble model object. Mdl.Trained stores the ensemble of 100 trained regression trees in a 100-by-1 cell array. That is, each cell in Mdl.Trained contains a CompactRegressionTree model object.

View a graph of the 10th regression tree in the ensemble.

Tree10 = Mdl.Trained{10};
view(Tree10,'Mode','graph');

By default, fitensemble grows shallow trees for boosted ensembles of trees.

Was this topic helpful?