CategoricalPredictors

Indices of categorical
predictors, stored as a vector of positive integers. CategoricalPredictors
contains index values corresponding to the columns of the predictor data that contain
categorical predictors. If none of the predictors are categorical, then this property is empty
([] ).

CategoricalSplits

An nby2 cell array, where n is
the number of categorical splits in tree . Each
row in CategoricalSplits gives left and right values
for a categorical split. For each branch node with categorical split j based
on a categorical predictor variable z , the left
child is chosen if z is in CategoricalSplits(j,1) and
the right child is chosen if z is in CategoricalSplits(j,2) .
The splits are in the same order as nodes of the tree. Nodes for these
splits can be found by running cuttype and selecting 'categorical' cuts
from top to bottom.

Children

An nby2 array containing the numbers of
the child nodes for each node in tree , where n is
the number of nodes. Leaf nodes have child node 0 .

CutCategories

An nby2 cell array of the categories used
at branches in tree , where n is
the number of nodes. For each branch node i based
on a categorical predictor variable x , the left
child is chosen if x is among the categories listed
in CutCategories{i,1} , and the right child is chosen
if x is among those listed in CutCategories{i,2} .
Both columns of CutCategories are empty for branch
nodes based on continuous predictors and for leaf nodes.
CutPoint contains the cut points for 'continuous' cuts,
and CutCategories contains the set of categories.

CutPoint

An nelement vector of the values used as
cut points in tree , where n is
the number of nodes. For each branch node i based
on a continuous predictor variable x , the left
child is chosen if x<CutPoint(i) and the right
child is chosen if x>=CutPoint(i) . CutPoint is NaN for
branch nodes based on categorical predictors and for leaf nodes.

CutType

An nelement cell array indicating the type
of cut at each node in tree , where n is
the number of nodes. For each node i , CutType{i} is:
'continuous' — If the cut
is defined in the form x < v for a variable x and
cut point v .
'categorical' — If the cut
is defined by whether a variable x takes a value
in a set of categories.
'' — If i is
a leaf node.
CutPoint contains the cut points for 'continuous' cuts,
and CutCategories contains the set of categories.

CutPredictor

An nelement cell array of the names of the
variables used for branching in each node in tree ,
where n is the number of nodes. These variables
are sometimes known as cut variables. For leaf nodes, CutPredictor contains
an empty character vector.
CutPoint contains the cut points for 'continuous' cuts,
and CutCategories contains the set of categories.

ExpandedPredictorNames

Expanded predictor names, stored as a cell array of character
vectors.
If the model uses encoding for categorical variables, then ExpandedPredictorNames includes
the names that describe the expanded variables. Otherwise, ExpandedPredictorNames is
the same as PredictorNames .

HyperparameterOptimizationResults

Description of the crossvalidation optimization of hyperparameters,
stored as a BayesianOptimization object
or a table of hyperparameters and associated values. Nonempty when
the OptimizeHyperparameters namevalue pair is
nonempty at creation. Value depends on the setting of the HyperparameterOptimizationOptions namevalue
pair at creation:
'bayesopt' (default) — Object
of class BayesianOptimization
'gridsearch' or 'randomsearch' —
Table of hyperparameters used, observed objective function values
(crossvalidation loss), and rank of observations from lowest (best)
to highest (worst)

IsBranchNode

An nelement logical vector ib that
is true for each branch node and false for
each leaf node of tree .

ModelParameters

Object holding parameters of tree .

NumObservations

Number of observations in the training data, a numeric scalar. NumObservations can
be less than the number of rows of input data X when
there are missing values in X or response Y .

NodeError

An nelement vector e of
the errors of the nodes in tree , where n is
the number of nodes. e(i) is the misclassification
probability for node i .

NodeMean

An nelement numeric array with mean values
in each node of tree , where n is
the number of nodes in the tree. Every element in NodeMean is
the average of the true Y values over all observations
in the node.

NodeProbability

An nelement vector p of
the probabilities of the nodes in tree , where n is
the number of nodes. The probability of a node is computed as the
proportion of observations from the original data that satisfy the
conditions for the node. This proportion is adjusted for any prior
probabilities assigned to each class.

NodeRisk

An nelement vector of the risk of the nodes
in the tree, where n is the number of nodes. The
risk for each node is the node error weighted by the node probability.

NodeSize

An nelement vector sizes of
the sizes of the nodes in tree , where n is
the number of nodes. The size of a node is defined as the number of
observations from the data used to create the tree that satisfy the
conditions for the node.

NumNodes

The number of nodes n in tree .

Parent

An nelement vector p containing
the number of the parent node for each node in tree ,
where n is the number of nodes. The parent of the
root node is 0 .

PredictorNames

A cell array of names for the predictor variables, in the order
in which they appear in X .

PruneAlpha

Numeric vector with one element per pruning level. If the pruning
level ranges from 0 to M, then PruneAlpha has M +
1 elements sorted in ascending order. PruneAlpha(1) is
for pruning level 0 (no pruning), PruneAlpha(2) is
for pruning level 1, and so on.

PruneList

An nelement numeric vector with the pruning
levels in each node of tree , where n is
the number of nodes. The pruning levels range from 0 (no pruning)
to M, where M is the distance
between the deepest leaf and the root node.

ResponseName

A character vector that specifies the name of the response variable
(Y ).

ResponseTransform

Function handle for transforming the raw response values (mean
squared error). The function handle must accept a matrix of response
values and return a matrix of the same size. The default 'none' means @(x)x ,
or no transformation.
Add or change a ResponseTransform function
using dot notation:
tree.ResponseTransform = @function

RowsUsed

An nelement logical vector indicating which
rows of the original predictor data (X ) were
used in fitting. If the software uses all rows of X ,
then RowsUsed is an empty array ([] ).

SurrogateCutCategories

An nelement cell array of the categories
used for surrogate splits in tree , where n is
the number of nodes in tree . For each node k , SurrogateCutCategories{k} is
a cell array. The length of SurrogateCutCategories{k} is
equal to the number of surrogate predictors found at this node. Every
element of SurrogateCutCategories{k} is either
an empty character vector for a continuous surrogate predictor, or
is a twoelement cell array with categories for a categorical surrogate
predictor. The first element of this twoelement cell array lists
categories assigned to the left child by this surrogate split, and
the second element of this twoelement cell array lists categories
assigned to the right child by this surrogate split. The order of
the surrogate split variables at each node is matched to the order
of variables in SurrogateCutPredictor . The optimalsplit
variable at this node does not appear. For nonbranch (leaf) nodes, SurrogateCutCategories contains
an empty cell.

SurrogateCutFlip

An nelement cell array of the numeric cut
assignments used for surrogate splits in tree ,
where n is the number of nodes in tree .
For each node k , SurrogateCutFlip{k} is
a numeric vector. The length of SurrogateCutFlip{k} is
equal to the number of surrogate predictors found at this node. Every
element of SurrogateCutFlip{k} is either zero for
a categorical surrogate predictor, or a numeric cut assignment for
a continuous surrogate predictor. The numeric cut assignment can be
either –1 or +1. For every surrogate split with a numeric cut C based
on a continuous predictor variable Z, the left
child is chosen if Z < C and the cut
assignment for this surrogate split is +1, or if Z ≥ C and
the cut assignment for this surrogate split is –1. Similarly,
the right child is chosen if Z ≥ C and
the cut assignment for this surrogate split is +1, or if Z < C and
the cut assignment for this surrogate split is –1. The order
of the surrogate split variables at each node is matched to the order
of variables in SurrogateCutPredictor . The optimalsplit
variable at this node does not appear. For nonbranch (leaf) nodes, SurrogateCutFlip contains
an empty array.

SurrogateCutPoint

An nelement cell array of the numeric values
used for surrogate splits in tree , where n is
the number of nodes in tree . For each node k , SurrogateCutPoint{k} is
a numeric vector. The length of SurrogateCutPoint{k} is
equal to the number of surrogate predictors found at this node. Every
element of SurrogateCutPoint{k} is either NaN for
a categorical surrogate predictor, or a numeric cut for a continuous
surrogate predictor. For every surrogate split with a numeric cut C based
on a continuous predictor variable Z, the left
child is chosen if Z<C and SurrogateCutFlip for
this surrogate split is +1, or if Z≥C and SurrogateCutFlip for
this surrogate split is –1. Similarly, the right child is chosen
if Z ≥ C and SurrogateCutFlip for
this surrogate split is +1, or if Z < C and SurrogateCutFlip for
this surrogate split is –1. The order of the surrogate split
variables at each node is matched to the order of variables returned
by SurrCutPredictor . The optimalsplit variable
at this node does not appear. For nonbranch (leaf) nodes, SurrogateCutPoint contains
an empty cell.

SurrogateCutType

An nelement cell array indicating types
of surrogate splits at each node in tree , where n is
the number of nodes in tree . For each node k , SurrogateCutType{k} is
a cell array with the types of the surrogate split variables at this
node. The variables are sorted by the predictive measure of association
with the optimal predictor in the descending order, and only variables
with the positive predictive measure are included. The order of the
surrogate split variables at each node is matched to the order of
variables in SurrogateCutPredictor . The optimalsplit
variable at this node does not appear. For nonbranch (leaf) nodes, SurrogateCutType contains
an empty cell. A surrogate split type can be either 'continuous' if
the cut is defined in the form Z < V for a variable Z and
cut point V or 'categorical' if
the cut is defined by whether Z takes a value in
a set of categories.

SurrogateCutPredictor

An nelement cell array of the names of the
variables used for surrogate splits in each node in tree ,
where n is the number of nodes in tree .
Every element of SurrogateCutPredictor is a cell
array with the names of the surrogate split variables at this node.
The variables are sorted by the predictive measure of association
with the optimal predictor in the descending order, and only variables
with the positive predictive measure are included. The optimalsplit
variable at this node does not appear. For nonbranch (leaf) nodes, SurrogateCutPredictor contains
an empty cell.

SurrogatePredictorAssociation

An nelement cell array of the predictive
measures of association for surrogate splits in tree ,
where n is the number of nodes in tree .
For each node k , SurrogatePredictorAssociation{k} is
a numeric vector. The length of SurrogatePredictorAssociation{k} is
equal to the number of surrogate predictors found at this node. Every
element of SurrogatePredictorAssociation{k} gives
the predictive measure of association between the optimal split and
this surrogate split. The order of the surrogate split variables at
each node is the order of variables in SurrogateCutPredictor .
The optimalsplit variable at this node does not appear. For nonbranch
(leaf) nodes, SurrogatePredictorAssociation contains
an empty cell.

W

The scaled weights , a vector with length n ,
the number of rows in X .

X

A matrix of predictor values. Each column of X represents
one variable, and each row represents one observation.

Y

A numeric column vector with the same number of rows as X .
Each entry in Y is the response to the data in
the corresponding row of X .
