CategoricalPredictors

List of categorical predictors, a numeric vector with indices
from 1 to p , where p is
the number of columns of X .

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. Find the nodes
for these splits by selecting 'categorical' cuts
from top to bottom in the CutType property.

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 .

ClassCount

An nbyk array of class
counts for the nodes in tree , where n is
the number of nodes and k is the number of classes.
For any node number i , the class counts ClassCount(i,:) are
counts of observations (from the data used in fitting the tree) from
each class satisfying the conditions for node i .

ClassNames

List of the elements in Y with duplicates
removed. ClassNames can be a categorical array,
cell array of strings, character array, logical vector, or a numeric
vector. ClassNames has the same data type as the
data in the argument Y .

ClassProbability

An nbyk array of class
probabilities for the nodes in tree , where n is
the number of nodes and k is the number of classes.
For any node number i , the class probabilities ClassProbability(i,:) are
the estimated probabilities for each class for a point satisfying
the conditions for node i .

Cost

Square matrix, where Cost(i,j) is the cost
of classifying a point into class j if its true
class is i (i.e., the rows correspond to the true
class and the columns correspond to the predicted class). The order
of the rows and columns of Cost corresponds to
the order of the classes in ClassNames .
The number of rows and columns in Cost is the number
of unique classes in the response. This property is readonly.

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.
CutPoint contains the cut points for 'continuous' cuts,
and CutCategories contains the set of categories.

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 string.
CutPoint contains the cut points for 'continuous' cuts,
and CutCategories contains the set of categories.

IsBranchNode

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

ModelParameters

Parameters used in training tree . To display
all parameter values, enter tree.ModelParameters .
To access a particular parameter, use dot notation.

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 .

NodeClass

An nelement cell array with the names of
the most probable classes in each node of tree ,
where n is the number of nodes in the tree. Every
element of this array is a string equal to one of the class names
in ClassNames .

NodeError

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

NodeProbability

An nelement vector 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 measure of impurity (Gini index or deviance)
for this node weighted by the node probability. If the tree is grown
by twoing, the risk for each node is zero.

NodeSize

An nelement vector 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 in tree .

Parent

An nelement vector 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

Cell array of strings containing the predictor names, in the
order which they appear in X .

Prior

Numeric vector of prior probabilities for each class. The order
of the elements of Prior corresponds to the order
of the classes in ClassNames .
The number of elements of Prior is the number of
unique classes in the response. This property is readonly.

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.

ResponseNames

String describing the response variable Y .

ScoreTransform

Function handle for transforming predicted classification scores,
or string representing a builtin transformation function.
none means no transformation, or @(x)x .
To change the score transformation function to, e.g., function ,
use dot notation. For available functions (see fitctree ),
enter Mdl.ScoreTransform = 'function'; You can set a function handle for an available function,
or a function you define yourself by entering tree.ScoreTransform = @function;

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 string 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 SurrogateCutPredictor . 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 categorical array, cell array of strings, character array,
logical vector, or a numeric vector. Each row of Y represents
the classification of the corresponding row of X .
