treefit - Fit tree

Note treefit will be removed in a future release. Use classregtree instead.

Syntax

t = treefit(X,y) t = treefit(X,y,param1,val1,param2,val2,...)

Description

Note This function is superseded by the classregtree constructor of the classregtree class and is maintained only for backwards compatibility. It returns objects t in the classregtree class.

t = treefit(X,y) creates a decision tree t for predicting response y as a function of predictors X. X is an n-by-m matrix of predictor values. y is either a vector of n response values (for regression), or a character array or cell array of strings containing n class names (for classification). Either way, t is a binary tree where each non-terminal node is split based on the values of a column of X.

t = treefit(X,y,param1,val1,param2,val2,...) specifies optional parameter name-value pairs. Valid parameter strings are:

The following table lists parameters available for all trees.

Parameter	Value
`'catidx'`	Vector of indices of the columns of `X`. `treefit` treats these columns as unordered categorical values.
`'method'`	Either `'classification'` (default if `y` is text) or `'regression'` (default if `y` is numeric).
`'splitmin'`	A number `n` such that impure nodes must have `n` or more observations to be split (default 10).
`'prune'`	`'on'` (default) to compute the full tree and a sequence of pruned subtrees, or `'off'` for the full tree without pruning.

The following table lists parameters available for classification trees only.

Parameter	Value
`'cost'`	`p`-by-`p` matrix `C`, where `p` is the number of distinct response values or class names in the input `y`. `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`.) `C` can also be a structure `S` with two fields: `S.group` containing the group names (see Grouped Data), and `S.cost` containing a matrix of cost values.
`'splitcriterion'`	Criterion for choosing a split: either `'gdi'` (default) for Gini's diversity index, `'twoing'` for the twoing rule, or `'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 `S` with two fields: `S.group` containing the group names, and `S.prob` containing a vector of corresponding probabilities.

Examples

Create a classification tree for Fisher's iris data:

  load fisheriris;
  t = treefit(meas,species);
  treedisp(t,'names',{'SL' 'SW' 'PL' 'PW'});

References

[1] Breiman, L., J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Boca Raton, FL: CRC Press, 1984.

treefit - Fit tree

Syntax

Description

Examples

References

See Also

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