Statistics Toolbox™

classregtree - Construct classification and regression trees

Class

@classregtree

Syntax

t = classregtree(X,y) t = classregtree(X,y,param1,val1,param2,val2)

Description

t = classregtree(X,y) creates a decision tree t for predicting the response y as a function of the predictors in the columns of X. X is an n-by-m matrix of predictor values. If y is a vector of n response values, classregtree performs regression. If y is a categorical variable, character array, or cell array of strings, classregtree performs classification. Either way, t is a binary tree where each branching node is split based on the values of a column of X. NaN values in X or y are taken as missing values, and observations with any missing values are not used in the fit.

t = classregtree(X,y,param1,val1,param2,val2) specifies optional parameter name/value pairs, as follows.

For all trees:

'categorical' — Vector of indices of the columns of X that are to be treated as unordered (nominal) categorical variables.
'method' — Either 'classification' (default if y is text or a categorical variable) or 'regression' (default if y is numeric).
'names' — A cell array of names for the predictor variables, in the order in which they appear in the X from which the tree was created.
'prune' — 'on' (default) to compute the full tree and the optimal sequence of pruned subtrees, or 'off' for the full tree without pruning.
'splitmin' — A number k such that impure nodes must have k or more observations to be split (default is 10).

For classification trees only:

'cost' — Square matrix C, where C(i,j) is the cost of classifying a point into class j if its true class is i. (The default has C(i,j) = 1 if i ~= j, and C(i,j) = 0 if i = j.) Alternatively, this value can be a structure with two fields:
- group — Containing the group names as a categorical array, a character array, or cell array of strings
- cost — Containing the cost matrix C
'splitcriterion' — Criterion for choosing a split. One of:
- 'gdi' — For Gini's diversity index (default)
- 'twoing' — For the twoing rule
- 'deviance' — For maximum deviance reduction
'priorprob' — Prior probabilities for each class, specified as a vector (one value for each distinct group name) or as a structure with two fields:
- group — Containing the group names as a categorical array, a character array, or cell array of strings
- prob — Containing a vector of corresponding probabilities

Example

Create a classification tree for Fisher's iris data:

load fisheriris;

t = classregtree(meas,species,...
                 'names',{'SL' 'SW' 'PL' 'PW'})
t = 
Decision tree for classification
1  if PL<2.45 then node 2 else node 3
2  class = setosa
3  if PW<1.75 then node 4 else node 5
4  if PL<4.95 then node 6 else node 7
5  class = virginica
6  if PW<1.65 then node 8 else node 9
7  class = virginica
8  class = versicolor
9  class = virginica

view(t)

Reference