Fit binary classification decision tree for multiclass classification

returns
a fitted binary classification decision tree based on the input variables
(also known as predictors, features, or attributes) contained in the
table `tree`

= fitctree(`TBL`

,`ResponseVarName`

)`TBL`

and output (response or labels) contained
in `ResponseVarName`

. The returned binary tree
splits branching nodes based on the values of a column of `TBL`

.

returns
a fitted binary classification decision tree based on the input variables
contained in the table `tree`

= fitctree(`TBL`

,`formula`

)`TBL`

. `formula`

is
a formula string that identifies the response and predictor variables
in `TBL`

used to fit `tree`

.
The returned binary tree splits branching nodes based on the values
of a column of `TBL`

.

fits
a tree with additional options specified by one or more name-value
pair arguments, using any of the previous syntaxes. For example, you
can specify the algorithm used to find the best split on a categorical
predictor, grow a cross-validated tree, or hold out a fraction of
the input data for validation.`tree`

= fitctree(___,`Name,Value`

)

Grow a classification tree using the `ionosphere`

data set.

```
load ionosphere
tc = fitctree(X,Y)
```

tc = ClassificationTree ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'b' 'g'} ScoreTransform: 'none' NumObservations: 351

You can control the depth of the trees using the `MaxNumSplits`

, `MinLeafSize`

, or `MinParentSize`

name-value pair parameters. `fitctree`

grows deep decision trees by default. You can grow shallower trees to reduce model complexity or computation time.

Load the `ionosphere`

data set.

```
load ionosphere
```

The default values of the tree depth controllers for growing classification trees are:

`n - 1`

for`MaxNumSplits`

.`n`

is the training sample size.`1`

for`MinLeafSize`

.`10`

for`MinParentSize`

.

These default values tend to grow deep trees for large training sample sizes.

Train a classification tree using the default values for tree depth control. Cross validate the model using 10-fold cross validation.

rng(1); % For reproducibility MdlDefault = fitctree(X,Y,'CrossVal','on');

Draw a histogram of the number of imposed splits on the trees. Also, view one of the trees.

numBranches = @(x)sum(x.IsBranch); mdlDefaultNumSplits = cellfun(numBranches, MdlDefault.Trained); figure; histogram(mdlDefaultNumSplits) view(MdlDefault.Trained{1},'Mode','graph')

The average number of splits is around 15.

Suppose that you want a classification tree that is not as complex (deep) as the ones trained using the default number of splits. Train another classification tree, but set the maximum number of splits at 7, which is about half the mean number of splits from the default classification tree. Cross validate the model using 10-fold cross validation.

Mdl7 = fitctree(X,Y,'MaxNumSplits',7,'CrossVal','on'); view(Mdl7.Trained{1},'Mode','graph')

Compare the cross validation classification errors of the models.

classErrorDefault = kfoldLoss(MdlDefault) classError7 = kfoldLoss(Mdl7)

classErrorDefault = 0.1140 classError7 = 0.1254

`Mdl7`

is much less complex and performs only slightly worse than `MdlDefault`

.

`TBL`

— Sample datatableSample data used to train the model, specified as a table. Each
row of `TBL`

corresponds to one observation, and
each column corresponds to one predictor variable. Optionally, `TBL`

can
contain one additional column for the response variable. Multi-column
variables and cell arrays other than cell arrays of strings are not
allowed.

If `TBL`

contains the response variable,
and you want to use all remaining variables in `TBL`

as
predictors, then specify the response variable using `ResponseVarName`

.

If `TBL`

contains the response variable,
and you want to use only a subset of the remaining variables in `TBL`

as
predictors, then specify a formula string using `formula`

.

If `TBL`

does not contain the response variable,
then specify a response variable using `Y`

. The
length of response variable and the number of rows of `TBL`

must
be equal.

**Data Types: **`table`

`X`

— Predictor valuesmatrix of floating-point valuesPredictor values, specified as a matrix of floating-point values.

`fitctree`

considers `NaN`

values
in `X`

as missing values. `fitctree`

does
not use observations with all missing values for `X`

in
the fit. `fitctree`

uses observations with some
missing values for `X`

to find splits on variables
for which these observations have valid values.

**Data Types: **`single`

| `double`

`ResponseVarName`

— Response variable namename of a variable in `TBL`

Response variable name, specified as the name of a variable
in `TBL`

.

You must specify `ResponseVarName`

as a string.
For example, if the response variable `Y`

is stored
as `TBL.Y`

, then specify it as `'Y'`

.
Otherwise, the software treats all columns of `TBL`

,
including `Y`

, as predictors when training the
model.

The response variable must be a categorical or character array,
logical or numeric vector, or cell array of strings. If `Y`

is
a character array, then each element must correspond to one row of
the array.

It is good practice to specify the order of the classes using
the `ClassNames`

name-value pair argument.

`formula`

— Response and predictor variables to use in model trainingstring in the form of `'Y~X1+X2+X3'`

Response and predictor variables to use in model training, specified
as a string in the form of `'Y~X1+X2+X3'`

. In this
form, `Y`

represents the response variable, and `X1`

, `X2`

,
and `X3`

represent the predictor variables.

To specify a subset of variables in `TBL`

as
predictors for training the model, use a formula string. If you specify
a formula string, then any variables in `TBL`

that
do not appear in `formula`

are not used to train
the model.

`Y`

— Class labelsnumeric vector | categorical vector | logical vector | character array | cell array of stringsClass labels, specified as a numeric vector, categorical vector,
logical vector, character array, or cell array of strings. Each row
of `X`

represents the classification of the corresponding
row of `X`

.

When fitting the tree, `fitctree`

considers `NaN`

, `''`

(empty
string), and `<undefined>`

values in `Y`

to
be missing values. `fitctree`

does not use observations
with missing values for `Y`

in the fit.

For numeric `Y`

, consider fitting a regression
tree using `fitrtree`

instead.

**Data Types: **`single`

| `double`

| `char`

| `logical`

| `cell`

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`

.

`'CrossVal','on','MinLeafSize',40`

specifies
a cross-validated classification tree with a minimum of 40 observations
per leaf.`'AlgorithmForCategorical'`

— Algorithm for best categorical predictor split`'Exact'`

| `'PullLeft'`

| `'PCA'`

| `'OVAbyClass'`

Algorithm to find the best split on a categorical predictor
with *C* categories for data and *K* ≥
3 classes, specified as the comma-separated pair consisting of `'AlgorithmForCategorical'`

and
one of the following.

`'Exact'` | Consider all 2^{C–1} –
1 combinations. |

`'PullLeft'` | Start with all C categories on the right
branch. Consider moving each category to the left branch as it achieves
the minimum impurity for the K classes among the
remaining categories. From this sequence, choose the split that has
the lowest impurity. |

`'PCA'` | Compute a score for each category using the inner product between
the first principal component of a weighted covariance matrix (of
the centered class probability matrix) and the vector of class probabilities
for that category. Sort the scores in ascending order, and consider
all C – 1 splits. |

`'OVAbyClass'` | Start with all C categories on the right
branch. For each class, order the categories based on their probability
for that class. For the first class, consider moving each category
to the left branch in order, recording the impurity criterion at each
move. Repeat for the remaining classes. From this sequence, choose
the split that has the minimum impurity. |

`fitctree`

automatically
selects the optimal subset of algorithms for each split using the
known number of classes and levels of a categorical predictor. For *K* =
2 classes, `fitctree`

always performs
the exact search. To specify a particular algorithm, use the `'AlgorithmForCategorical'`

name-value
pair argument.

**Example: **`'AlgorithmForCategorical','PCA'`

`'CategoricalPredictors'`

— Categorical predictors listnumeric or logical vector | cell array of strings | character matrix | `'all'`

Categorical predictors list, specified as the comma-separated
pair consisting of `'CategoricalPredictors'`

and
one of the following:

A numeric vector with indices from

`1`

through`p`

, where`p`

is the number of columns of`X`

.A logical vector of length

`p`

, where a`true`

entry means that the corresponding column of`X`

is a categorical variable.A cell array of strings, where each element in the array is the name of a predictor variable. The names must match entries in

`PredictorNames`

values.A character matrix, where each row of the matrix is a name of a predictor variable. The names must match entries in

`PredictorNames`

values. Pad the names with extra blanks so each row of the character matrix has the same length.`'all'`

, meaning all predictors are categorical.

By default, if the predictor data is in a matrix (`X`

),
the software assumes that none of the predictors are categorical.
If the predictor data is in a table (`TBL`

), the
software assumes that a variable is categorical if it contains, logical
values, values of the unordered data type `categorical`

,
or a cell array of strings.

**Example: **`'CategoricalPredictors','all'`

**Data Types: **`single`

| `double`

| `char`

`'ClassNames'`

— Names of classes to use for trainingcategorical array | cell array of strings | character array | logical vector | numeric vectorNames of classes to use for training, specified as the comma-separated
pair consisting of `'ClassNames'`

and a categorical
or character array, logical or numeric vector, or cell array of strings. `ClassNames`

must
be the same data type as `Y`

.

If `ClassNames`

is a character array, then
each element must correspond to one *row* of the
array.

Use `ClassNames`

to:

Order the classes during training.

Specify the order of any input or output argument dimension that corresponds to the class order. For example, use

`ClassNames`

to specify the order of the dimensions of`Cost`

or the column order of classification scores returned by`predict`

.Select a subset of classes for training. For example, suppose that the set of all distinct class names in

`Y`

is`{'a','b','c'}`

. To train the model using observations from classes`'a'`

and`'c'`

only, specify`'ClassNames',{'a','c'}`

.

The default is the set of all distinct class names in `Y`

.

**Example: **`'ClassNames',{'b','g'}`

**Data Types: **`categorical`

| `char`

| `logical`

| `single`

| `double`

| `cell`

`'Cost'`

— Cost of misclassificationsquare matrix | structureCost of misclassification of a point, specified as the comma-separated
pair consisting of `'Cost'`

and one of the following:

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). To specify the class order for the corresponding rows and columns of`Cost`

, also specify the`ClassNames`

name-value pair argument.Structure

`S`

having two fields:`S.ClassNames`

containing the group names as a variable of the same data type as`Y`

, and`S.ClassificationCosts`

containing the cost matrix.

The default is `Cost(i,j)=1`

if `i~=j`

,
and `Cost(i,j)=0`

if `i=j`

.

**Data Types: **`single`

| `double`

| `struct`

`'CrossVal'`

— Flag to grow cross-validated decision tree`'off'`

(default) | `'on'`

Flag to grow a cross-validated decision tree, specified as
the comma-separated pair consisting of `'CrossVal'`

and `'on'`

or `'off'`

.

If `'on'`

, `fitctree`

grows
a cross-validated decision tree with 10 folds. You can override this
cross-validation setting using one of the `'KFold'`

, `'Holdout'`

, `'Leaveout'`

,
or `'CVPartition'`

name-value pair arguments. You
can only use one of these four arguments at a time when creating a
cross-validated tree.

Alternatively, cross validate `tree`

later
using the `crossval`

method.

**Example: **`'CrossVal','on'`

`'CVPartition'`

— Partition for cross-validated tree`cvpartition`

objectPartition to use in a cross-validated tree, specified as the
comma-separated pair consisting of `'CVPartition'`

and
an object created using `cvpartition`

.

If you use `'CVPartition'`

, you cannot use
any of the `'KFold'`

, `'Holdout'`

,
or `'Leaveout'`

name-value pair arguments.

`'Holdout'`

— Fraction of data for holdout validation`0`

(default) | scalar value in the range `[0,1]`

Fraction of data used for holdout validation, specified as the
comma-separated pair consisting of `'Holdout'`

and
a scalar value in the range `[0,1]`

. Holdout validation
tests the specified fraction of the data, and uses the rest of the
data for training.

If you use `'Holdout'`

, you cannot use any
of the `'CVPartition'`

, `'KFold'`

,
or `'Leaveout'`

name-value pair arguments.

**Example: **`'Holdout',0.1`

**Data Types: **`single`

| `double`

`'KFold'`

, `'KFold'`

— Number of folds`10`

(default) | positive integer value greater than 1Number of folds to use in a cross-validated classifier, specified
as the comma-separated pair consisting of `'KFold'`

and
a positive integer value greater than 1. If you specify, e.g., `'KFold',`

,
then the software:*k*

Randomly partitions the data into

*k*setsFor each set, reserves the set as validation data, and trains the model using the other

*k*– 1 setsStores the

compact, trained models in the cells of a`k`

-by-1 cell vector in the`k`

`Trained`

property of the cross-validated model.

To create a cross-validated model, you can use one of these
four options only: `'`

`CVPartition`

`'`

, `'`

`Holdout`

`'`

, `'`

`KFold`

`'`

,
or `'`

`Leaveout`

`'`

.

**Example: **`'KFold',8`

**Data Types: **`single`

| `double`

`'Leaveout'`

— Leave-one-out cross-validation flag`'off'`

(default) | `'on'`

Leave-one-out cross-validation flag, specified as the comma-separated
pair consisting of `'Leaveout'`

and `'on'`

or `'off'`

.
Specify `'on'`

to use leave-one-out cross-validation.

If you use `'Leaveout'`

, you cannot use any
of the `'CVPartition'`

, `'Holdout'`

,
or `'KFold'`

name-value pair arguments.

**Example: **`'Leaveout','on'`

`'MaxNumCategories'`

— Maximum category levels`10`

(default) | nonnegative scalar valueMaximum category levels, specified as the comma-separated pair
consisting of `'MaxNumCategories'`

and a nonnegative
scalar value. `fitctree`

splits
a categorical predictor using the exact search algorithm if the predictor
has at most `MaxNumCategories`

levels in the split
node. Otherwise, `fitctree`

finds
the best categorical split using one of the inexact algorithms.

Passing a small value can lead to loss of accuracy and passing a large value can increase computation time and memory overload.

**Example: **`'MaxNumCategories',8`

`'MaxNumSplits'`

— Maximal number of decision splits`size(X,1) - 1`

(default) | positive integerMaximal number of decision splits (or branch nodes), specified
as the comma-separated pair consisting of `'MaxNumSplits'`

and
a positive integer. `fitctree`

splits `MaxNumSplits`

or
fewer branch nodes. For more details on splitting behavior, see Algorithms.

**Example: **`'MaxNumSplits',5`

**Data Types: **`single`

| `double`

`'MergeLeaves'`

— Leaf merge flag`'on'`

(default) | `'off'`

Leaf merge flag, specified as the comma-separated pair consisting
of `'MergeLeaves'`

and `'on'`

or `'off'`

.

If `MergeLeaves`

is `'on'`

,
then `fitctree`

:

Merges leaves that originate from the same parent node, and that yields a sum of risk values greater or equal to the risk associated with the parent node

Estimates the optimal sequence of pruned subtrees, but does not prune the classification tree

Otherwise, `fitctree`

does not merge leaves.

**Example: **`'MergeLeaves','off'`

`'MinLeafSize'`

— Minimum number of leaf node observations`1`

(default) | positive integer valueMinimum number of leaf node observations, specified as the comma-separated
pair consisting of `'MinLeafSize'`

and a positive
integer value. Each leaf has at least `MinLeafSize`

observations
per tree leaf. If you supply both `MinParentSize`

and `MinLeafSize`

, `fitctree`

uses
the setting that gives larger leaves: `MinParentSize = max(MinParentSize,2*MinLeafSize)`

.

**Example: **`'MinLeafSize',3`

**Data Types: **`single`

| `double`

`'MinParentSize'`

— Minimum number of branch node observations`10`

(default) | positive integer valueMinimum number of branch node observations, specified as the
comma-separated pair consisting of `'MinParentSize'`

and
a positive integer value. Each branch node in the tree has at least `MinParentSize`

observations.
If you supply both `MinParentSize`

and `MinLeafSize`

, `fitctree`

uses the setting that gives larger
leaves: `MinParentSize = max(MinParentSize,2*MinLeafSize)`

.

**Example: **`'MinParentSize',8`

**Data Types: **`single`

| `double`

`'NumVariablesToSample'`

— Number of predictors to select at random for each split`'all'`

| positive integer valueNumber of predictors to select at random for each split, specified
as the comma-separated pair consisting of `'NumVariablesToSample'`

and
a positive integer value. You can also specify `'all'`

to
use all available predictors.

**Example: **`'NumVariablesToSample',3`

**Data Types: **`single`

| `double`

`'PredictorNames'`

— Predictor variable names`{'x1','x2',...}`

(default) | cell array of stringsPredictor variable names, specified as the comma-separated pair
consisting of `'PredictorNames'`

and a cell array
of strings containing the names for the predictor variables, in the
order in which they appear in `X`

or `TBL`

.

If you specify the predictors as a table (`TBL`

), `PredictorNames`

must
be a subset of the variable names in `TBL`

. In
this case, the software uses only the variables in `PredictorNames`

to
fit the model. If you use formula to specify the model, then you cannot
use the `PredictorNames`

name-value pair.

**Example: **`'PredictorNames',{'PetalWidth','PetalLength'}`

`'Prior'`

— Prior probabilities`'empirical'`

(default) | `'uniform'`

| vector of scalar values | structurePrior probabilities for each class, specified as the comma-separated
pair consisting of `'Prior'`

and one of the following.

A string:

`'empirical'`

determines class probabilities from class frequencies in`Y`

. If you pass observation weights,`fitctree`

uses the weights to compute the class probabilities.`'uniform'`

sets all class probabilities equal.

A vector (one scalar value for each class). To specify the class order for the corresponding elements of

`Prior`

, also specify the`ClassNames`

name-value pair argument.A structure

`S`

with two fields:`S.ClassNames`

containing the class names as a variable of the same type as`Y`

`S.ClassProbs`

containing a vector of corresponding probabilities

If you set values for both `weights`

and `prior`

,
the weights are renormalized to add up to the value of the prior probability
in the respective class.

**Example: **`'Prior','uniform'`

`'Prune'`

— Flag to estimate optimal sequence of pruned subtrees`'on'`

(default) | `'off'`

Flag to estimate the optimal sequence of pruned subtrees, specified
as the comma-separated pair consisting of `'Prune'`

and `'on'`

or `'off'`

.

If `Prune`

is `'on'`

, then `fitctree`

grows
the classification tree without pruning it, but estimates the optimal
sequence of pruned subtrees. Otherwise, `fitctree`

grows
the classification tree without estimating the optimal sequence of
pruned subtrees.

To prune a trained `ClassificationTree`

model, pass it to `prune`

.

**Example: **`'Prune','off'`

`'PruneCriterion'`

— Pruning criterion`'error'`

(default) | `'impurity'`

Pruning criterion, specified as the comma-separated pair consisting
of `'PruneCriterion'`

and `'error'`

or `'impurity'`

.

**Example: **`'PruneCriterion','impurity'`

`'ResponseName'`

— Response variable name`'Y'`

(default) | stringResponse variable name, specified as the comma-separated pair
consisting of `'ResponseName'`

and a string representing
the name of the response variable.

This name-value pair is not valid when using the `ResponseVarName`

or `formula`

input
arguments.

**Example: **`'ResponseName','IrisType'`

`'ScoreTransform'`

— Score transform function`'none'`

| `'symmetric'`

| `'invlogit'`

| `'ismax'`

| function handle | ...Score transform function, specified as the comma-separated pair
consisting of `'ScoreTransform'`

and a function handle
for transforming scores. Your function must accept a matrix (the original
scores) and return a matrix of the same size (the transformed scores).

Alternatively, you can specify one of the following strings representing a built-in transformation function.

String | Formula |
---|---|

`'doublelogit'` | 1/(1 + e^{–2x}) |

`'invlogit'` | log(x / (1–x)) |

`'ismax'` | Set the score for the class with the largest score to `1` ,
and scores for all other classes to `0` . |

`'logit'` | 1/(1 + e^{–x}) |

`'none'` or `'identity'` | x (no transformation) |

`'sign'` | –1 for x < 00 for x = 01 for x >
0 |

`'symmetric'` | 2x – 1 |

`'symmetriclogit'` | 2/(1 + e^{–x})
– 1 |

`'symmetricismax'` | Set the score for the class with the largest score to `1` ,
and scores for all other classes to `-1` . |

**Example: **`'ScoreTransform','logit'`

`'SplitCriterion'`

— Split criterion`'gdi'`

(default) | `'twoing'`

| `'deviance'`

Split criterion, specified as the comma-separated pair consisting
of `'SplitCriterion'`

and `'gdi'`

(Gini's
diversity index), `'twoing'`

for the twoing rule,
or `'deviance'`

for maximum deviance reduction (also
known as cross entropy).

**Example: **`'SplitCriterion','deviance'`

`'Surrogate'`

— Surrogate decision splits flag`'off'`

| `'on'`

| `'all'`

| positive integer valueSurrogate
decision splits flag, specified as the comma-separated pair
consisting of `'Surrogate'`

and `'on'`

, `'off'`

, `'all'`

,
or a positive integer value.

When set to

`'on'`

,`fitctree`

finds at most 10 surrogate splits at each branch node.When set to

`'all'`

,`fitctree`

finds all surrogate splits at each branch node. The`'all'`

setting can use considerable time and memory.When set to a positive integer value,

`fitctree`

finds at most the specified number of surrogate splits at each branch node.

Use surrogate splits to improve the accuracy of predictions for data with missing values. The setting also lets you compute measures of predictive association between predictors. For more details, see Node Splitting Rules.

**Example: **`'Surrogate','on'`

`'Weights'`

— Observation weights`ones(size(x,1),1)`

(default) | vector of scalar valuesObservation weights, specified as the comma-separated pair consisting
of `'Weights'`

and a vector of scalar values. The
software weights the observations in each row of `X`

or `TBL`

with
the corresponding value in `Weights`

. The size of `Weights`

must
equal the number of rows in `X`

or `TBL`

.

If you specify the input data as a table `TBL`

,
then `Weights`

can be the name of a variable in `TBL`

that
contains a numeric vector. In this case, you must specify `Weights`

as
a variable name string. For example, if weights vector `W`

is
stored as `TBL.W`

, then specify it as `'W'`

.
Otherwise, the software treats all columns of `TBL`

,
including `W`

, as predictors when training the model.

`fitctree`

normalizes the
weights in each class to add up to the value of the prior probability
of the class.

**Data Types: **`single`

| `double`

`tree`

— Classification treeclassification tree objectClassification tree, returned as a classification tree object.

Using the `'CrossVal'`

, `'KFold'`

, `'Holdout'`

, `'Leaveout'`

,
or `'CVPartition'`

options results in a tree of class `ClassificationPartitionedModel`

.
You cannot use a partitioned tree for prediction, so this kind of
tree does not have a `predict`

method. Instead, use `kfoldpredict`

to predict responses for observations
not used for training.

Otherwise, `tree`

is of class `ClassificationTree`

,
and you can use the `predict`

method to make predictions.

`ClassificationTree`

splits
nodes based on either *impurity* or *node
error*.

Impurity means one of several things, depending on your choice
of the `SplitCriterion`

name-value pair argument:

Gini's Diversity Index (

`gdi`

) — The Gini index of a node is$$1-{\displaystyle \sum _{i}{p}^{2}(i)},$$

where the sum is over the classes

*i*at the node, and*p*(*i*) is the observed fraction of classes with class*i*that reach the node. A node with just one class (a*pure*node) has Gini index`0`

; otherwise the Gini index is positive. So the Gini index is a measure of node impurity.Deviance (

`'deviance'`

) — With*p*(*i*) defined the same as for the Gini index, the deviance of a node is$$-{\displaystyle \sum _{i}p(i)\mathrm{log}p(i)}.$$

A pure node has deviance

`0`

; otherwise, the deviance is positive.Twoing rule (

`'twoing'`

) — Twoing is not a purity measure of a node, but is a different measure for deciding how to split a node. Let*L*(*i*) denote the fraction of members of class*i*in the left child node after a split, and*R*(*i*) denote the fraction of members of class*i*in the right child node after a split. Choose the split criterion to maximize$$P(L)P(R){\left({\displaystyle \sum _{i}\left|L(i)-R(i)\right|}\right)}^{2},$$

where

*P*(*L*) and*P*(*R*) are the fractions of observations that split to the left and right respectively. If the expression is large, the split made each child node purer. Similarly, if the expression is small, the split made each child node similar to each other, and hence similar to the parent node, and so the split did not increase node purity.Node error — The node error is the fraction of misclassified classes at a node. If

*j*is the class with the largest number of training samples at a node, the node error is1 –

*p*(*j*).

The *predictive measure of association* is
a value that indicates the similarity between decision rules that
split observations. Among all possible decision splits that are compared
to the optimal split (found by growing the tree), the best surrogate decision
split yields the maximum predictive measure of association.
The second-best surrogate split has the second-largest predictive
measure of association.

Suppose *x _{j}* and

$${\lambda}_{jk}=\frac{\text{min}\left({P}_{L},{P}_{R}\right)-\left(1-{P}_{{L}_{j}{L}_{k}}-{P}_{{R}_{j}{R}_{k}}\right)}{\text{min}\left({P}_{L},{P}_{R}\right)}.$$

*P*is the proportion of observations in node_{L}*t*, such that*x*<_{j}*u*. The subscript*L*stands for the left child of node*t*.*P*is the proportion of observations in node_{R}*t*, such that*x*≥_{j}*u*. The subscript*R*stands for the right child of node*t*.$${P}_{{L}_{j}{L}_{k}}$$ is the proportion of observations at node

*t*, such that*x*<_{j}*u*and*x*<_{k}*v*.$${P}_{{R}_{j}{R}_{k}}$$ is the proportion of observations at node

*t*, such that*x*≥_{j}*u*and*x*≥_{k}*v*.Observations with missing values for

*x*or_{j}*x*do not contribute to the proportion calculations._{k}

*λ _{jk}* is a value
in (–∞,1]. If

A *surrogate decision split* is an alternative
to the optimal decision split at a given node in a decision tree.
The optimal split is found by growing the tree; the surrogate split
uses a similar or correlated predictor variable and split criterion.

When the value of the optimal split predictor for an observation is missing, the observation is sent to the left or right child node using the best surrogate predictor. When the value of the best surrogate split predictor for the observation is also missing, the observation is sent to the left or right child node using the second-best surrogate predictor, and so on. Candidate splits are sorted in descending order by their predictive measure of association.

`fitctree`

follows these steps to determine
how to split node *t*. For all predictors *x _{i}*,

`fitctree`

computes the weighted impurity of node*t*,*i*. For supported impurity measures, see_{t}`SplitCriterion`

.`fitctree`

estimates the probability that an observation is in node*t*using$$P\left(T\right)={\displaystyle \sum _{j\in T}{w}_{j}}.$$

*w*is the weight of observation_{j}*j*, and*T*is the set of all observation indices in node*t*. If you do not specify`Prior`

or`Weights`

, then*w*= 1/_{j}*n*, where*n*is the sample size.`fitctree`

sorts*x*in ascending order. Each element of the sorted predictor is a splitting candidate or cut point._{i}`fitctree`

stores any indices corresponding to missing values in the set*T*, which is the unsplit set._{U}`fitctree`

determines the best way to split node*t*using*x*by maximizing the impurity gain (Δ_{i}*I*) over all splitting candidates. That is, for all splitting candidates in*x*:_{i}`fitctree`

splits the observations in node*t*into left and right child nodes (*t*and_{L}*t*, respectively)._{R}`fitctree`

computes Δ*I*. Suppose that for a particular splitting candidate,*t*and_{L}*t*contain observation indices in the sets_{R}*T*and_{L}*T*, respectively._{R}If

*x*does not contain any missing values, then the impurity gain for the current splitting candidate is_{i}$$\Delta I=P\left(T\right){i}_{t}-P\left({T}_{L}\right){i}_{{t}_{L}}-P\left({T}_{R}\right){i}_{{t}_{R}}.$$

If

*x*contains missing values then, assuming that the observations are missing at random, the impurity gain is_{i}$$\Delta {I}_{U}=P\left(T-{T}_{U}\right){i}_{t}-P\left({T}_{L}\right){i}_{{t}_{L}}-P\left({T}_{R}\right){i}_{{t}_{R}}.$$

*T*–*T*is the set of all observation indices in node_{U}*t*that are not missing.If you use surrogate decision splits, then:

`fitctree`

computes the predictive measures of association between the decision split*x*<_{j}*u*and all possible decision splits*x*<_{k}*v*,*j*≠*k*.`fitctree`

sorts the possible alternative decision splits in descending order by their predictive measure of association with the optimal split. The surrogate split is the decision split yielding the largest measure.`fitctree`

decides the child node assignments for observations with a missing value for*x*using the surrogate split. If the surrogate predictor also contains a missing value, then_{i}`fitctree`

uses the decision split with the second largest measure, and so on, until there are no other surrogates. It is possible for`fitctree`

to split two different observations at node*t*using two different surrogate splits. For example, suppose the predictors*x*_{1}and*x*_{2}are the best and second best surrogates, respectively, for the predictor*x*,_{i}*i*∉ {1,2}, at node*t*. If observation*m*of predictor*x*is missing (i.e.,_{i}*x*is missing), but_{mi}*x*_{m1}is not missing, then*x*_{1}is the surrogate predictor for observation*x*. If observations_{mi}*x*_{(m + 1),i}and*x*(*m*+ 1),*1*are missing, but*x*_{(m + 1),2}is not missing, then*x*_{2}is the surrogate predictor for observation*m*+ 1.`fitctree`

uses the appropriate impurity gain formula. That is, if`fitctree`

fails to assign all missing observations in node*t*to children nodes using surrogate splits, then the impurity gain is Δ*I*. Otherwise,_{U}`fitctree`

uses Δ*I*for the impurity gain.

`fitctree`

chooses the candidate that yields the largest impurity gain.

`fitctree`

splits the predictor
variable at the cut point that maximizes the impurity gain.

If

`MergeLeaves`

is`'on'`

and`PruneCriterion`

is`'error'`

(which are the default values for these name-value pair arguments), then the software applies pruning only to the leaves and by using classification error. This specification amounts to merging leaves that share the most popular class per leaf.To accommodate

`MaxNumSplits`

,`fitctree`

splits all nodes in the current*layer*, and then counts the number of branch nodes. A layer is the set of nodes that are equidistant from the root node. If the number of branch nodes exceeds`MaxNumSplits`

,`fitctree`

follows this procedure:Determine how many branch nodes in the current layer must be unsplit so that there are at most

`MaxNumSplits`

branch nodes.Sort the branch nodes by their impurity gains.

Unsplit the number of least successful branches.

Return the decision tree grown so far.

This procedure produces maximally balanced trees.

The software splits branch nodes layer by layer until at least one of these events occurs:

There are

`MaxNumSplits`

branch nodes.A proposed split causes the number of observations in at least one branch node to be fewer than

`MinParentSize`

.A proposed split causes the number of observations in at least one leaf node to be fewer than

`MinLeafSize`

.The algorithm cannot find a good split within a layer (i.e., the pruning criterion (see

`PruneCriterion`

), does not improve for all proposed splits in a layer). A special case is when all nodes are pure (i.e., all observations in the node have the same class).

`MaxNumSplits`

and`MinLeafSize`

do not affect splitting at their default values. Therefore, if you set`'MaxNumSplits'`

, splitting might stop due to the value of`MinParentSize`

, before`MaxNumSplits`

splits occur.

For dual-core systems and above, `fitctree`

parallelizes
training decision trees using Intel^{®} Threading Building Blocks
(TBB). For details on Intel TBB, see https://software.intel.com/en-us/intel-tbb.

[1] Coppersmith, D., S. J. Hong, and J. R.
M. Hosking. "Partitioning Nominal Attributes in Decision Trees." *Data
Mining and Knowledge Discovery*, Vol. 3, 1999, pp. 197–217.

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

`ClassificationPartitionedModel`

| `ClassificationTree`

| `kfoldpredict`

| `predict`

| `prune`

You clicked a link that corresponds to this MATLAB command:

Run the command by entering it in the MATLAB Command Window. Web browsers do not support MATLAB commands.

Was this topic helpful?

You can also select a location from the following list:

- Canada (English)
- United States (English)

- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)

- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)