Fit binary regression decision tree

returns
a regression tree based on the input variables (also known as predictors,
features, or attributes) in the table `tree`

= fitrtree(`tbl`

,`ResponseVarName`

)`tbl`

and
output (response) contained in `tbl.ResponseVarName`

. `tree`

is
a binary tree where each branching node is split based on the values
of a column of `tbl`

.

fits
a tree with additional options specified by one or more `tree`

= fitrtree(___,`Name,Value`

)`Name,Value`

pair
arguments. For example, you can specify observation weights or train
a cross-validated model.

If you use one of the following five options, `tree`

is
of class `RegressionPartitionedModel`

: `'CrossVal'`

, `'KFold'`

, `'Holdout'`

, `'Leaveout'`

,
or `'CVPartition'`

. Otherwise, `tree`

is
of class `RegressionTree`

.

Load the sample data.

```
load carsmall;
```

Construct a regression tree using the sample data.

tree = fitrtree([Weight, Cylinders],MPG,... 'categoricalpredictors',2,'MinParentSize',20,... 'PredictorNames',{'W','C'})

tree = RegressionTree PredictorNames: {'W' 'C'} ResponseName: 'Y' CategoricalPredictors: 2 ResponseTransform: 'none' NumObservations: 94

Predict the mileage of 4,000-pound cars with 4, 6, and 8 cylinders.

mileage4K = predict(tree,[4000 4; 4000 6; 4000 8])

mileage4K = 19.2778 19.2778 14.3889

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

, `MinLeafSize`

, or `MinParentSize`

name-value pair parameters. `fitrtree`

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

Load the `carsmall`

data set. Consider `Displacement`

, `Horsepower`

, and `Weight`

as predictors of the response `MPG`

.

```
load carsmall
X = [Displacement Horsepower Weight];
```

The default values of the tree-depth controllers for growing regression 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 regression tree using the default values for tree-depth control. Cross validate the model using 10-fold cross validation.

rng(1); % For reproducibility MdlDefault = fitrtree(X,MPG,'CrossVal','on');

Draw a histogram of the number of imposed on the trees. The number of imposed splits is one less than the number of leaves. 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 between 14 and 15.

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

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

Compare the cross validation MSEs of the models.

mseDefault = kfoldLoss(MdlDefault) mse7 = kfoldLoss(Mdl7)

mseDefault = 27.7277 mse7 = 28.3833

`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 matrix of floating-point values.
Each column of `x`

represents one variable, and
each row represents one observation.

`fitrtree`

considers `NaN`

values
in `x`

as missing values. `fitrtree`

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

the
fit. `fitrtree`

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 `'response'`

.
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`

— Response datanumeric column vectorResponse data, specified as 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`

.

`fitrtree`

considers `NaN`

values
in `y`

to be missing values. `fitrtree`

does
not use observations with missing values for `y`

in
the fit.

**Data Types: **`single`

| `double`

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','MinParentSize',30`

specifies
a cross-validated regression tree with a minimum of 30 observations
per branch node.`'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`

to`p`

, where`p`

is the number of columns of`x`

or`tbl`

.A logical vector of length

`p`

, where a`true`

entry means that the corresponding column of`x`

or`tbl`

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 the

`PredictorNames`

property.A character matrix, where each row of the matrix is a name of a predictor variable. 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.

**Data Types: **`single`

| `double`

| `logical`

| `char`

| `cell`

`'CrossVal'`

— Cross-validation flag`'off'`

(default) | `'on'`

Cross-validation flag, specified as the comma-separated pair
consisting of `'CrossVal'`

and either `'on'`

or `'off'`

.

If `'on'`

, `fitrtree`

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 options (`'KFold'`

, `'Holdout'`

, `'Leaveout'`

,
or `'CVPartition'`

) 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-validation tree`cvpartition`

objectPartition for 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'`

— Number of folds`10`

(default) | positive integer valueNumber of folds to use in a cross-validated tree, specified
as the comma-separated pair consisting of `'KFold'`

and
a positive integer value.

If you use `'KFold'`

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

, `'Holdout'`

,
or `'Leaveout'`

name-value pair arguments.

**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 either `'on'`

or `'off`

.
Use leave-one-out cross validation by setting to `'on'`

.

If you use `'Leaveout'`

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

, `'Holdout'`

,
or `'KFold'`

name-value pair arguments.

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

`'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 `fitrtree`

:

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 regression tree

Otherwise, `fitrtree`

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`

, `fitrtree`

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`

, `fitrtree`

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

.

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

**Data Types: **`single`

| `double`

`'NumVariablesToSample'`

— Number of predictors for split`'all'`

(default) | 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.

**Data Types: **`cell`

`'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 `fitrtree`

grows
the regression tree and estimates the optimal sequence of pruned subtrees,
but does not prune the regression tree. Otherwise, `fitrtree`

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

To prune a trained regression tree, pass the regression tree
to `prune`

.

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

`'PruneCriterion'`

— Pruning criterion`'error'`

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

and `'error'`

.

**Example: **`'PruneCriterion','error'`

`'QuadraticErrorTolerance'`

— Quadratic error tolerance`1e-6`

(default) | positive scalar valueQuadratic error tolerance per node, specified as the comma-separated
pair consisting of `'QuadraticErrorTolerance'`

and
a positive scalar value. Splitting nodes stops when quadratic error
per node drops below `QuadraticErrorTolerance*QED`

,
where `QED`

is the quadratic error for the entire
data computed before the decision tree is grown.

**Example: **`'QuadraticErrorTolerance',1e-4`

`'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','Response'`

**Data Types: **`char`

`'ResponseTransform'`

— Response transform function`'none'`

(default) | function handleResponse transform function for transforming the raw response
values, specified as the comma-separated pair consisting of `'ResponseTransform'`

and
either a function handle or `'none'`

. The function
handle must accept a matrix of response values and return a matrix
of the same size. The default string `'none'`

means `@(x)x`

,
or no transformation.

Add or change a `ResponseTransform`

function
using dot notation:

tree.ResponseTransform = @function

**Data Types: **`function_handle`

`'SplitCriterion'`

— Split criterion`'MSE'`

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

and `'MSE'`

,
meaning mean squared error.

**Example: **`'SplitCriterion','MSE'`

`'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

`'on'`

,`fitrtree`

finds at most 10 surrogate splits at each branch node.When set to a positive integer value,

`fitrtree`

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

`'all'`

,`fitrtree`

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

setting can use much time and memory.

Use surrogate splits to improve the accuracy of predictions for data with missing values. The setting also enables you to compute measures of predictive association between predictors.

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

**Data Types: **`single`

| `double`

`'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.

`fitrtree`

normalizes the
weights in each class to add up to 1.

**Data Types: **`single`

| `double`

`tree`

— Regression treeregression tree objectRegression tree, returned as a regression tree object. Using
the `'Crossval'`

, `'KFold'`

, `'Holdout'`

, `'Leaveout'`

,
or `'CVPartition'`

options results in a tree of class `RegressionPartitionedModel`

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

method.

Otherwise, `tree`

is of class `RegressionTree`

, and
you can use the `predict`

method to make predictions.

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.

At node *t*, the predictive measure of association
between the optimal split *x _{j}* <

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

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

*t*, such that*x*≥_{j}*s*and_{j}*x*≥_{k}*s*._{k}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.

Suppose that the optimal splitting criterion at node *t* is *x _{j}* <

`fitrtree`

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

`fitrtree`

computes the weighted, mean-square error (MSE) of the responses in node*t*using$${\epsilon}_{t}={\displaystyle \sum _{j\in T}{w}_{j}}{\left({y}_{j}-{\overline{y}}_{t}\right)}^{2}.$$

*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`Weights`

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

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

`fitrtree`

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

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

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

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

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 reduction in MSE for the current splitting candidate is_{i}$$\Delta I=P\left(T\right){\epsilon}_{t}-P\left({T}_{L}\right){\epsilon}_{{t}_{L}}-P\left({T}_{R}\right){\epsilon}_{{t}_{R}}.$$

If

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

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

`fitrtree`

computes the predictive measures of association between the decision split*x*<_{i}*s*and all possible decision splits_{i}*x*<_{k}*s*,_{k}*k*≠*i*.`fitrtree`

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.`fitrtree`

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}`fitrtree`

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

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.`fitrtree`

uses the appropriate MSE reduction formula. That is, if`fitrtree`

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

uses Δ*I*for the MSE reduction.

`fitrtree`

chooses the candidate that yields the largest MSE reduction.

`fitrtree`

splits the predictor
variable at the cut point that maximizes the MSE reduction.

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`

,`fitrtree`

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`

,`fitrtree`

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, `fitrtree`

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

`predict`

| `prune`

| `RegressionPartitionedModel`

| `RegressionTree`

| `surrogateAssociation`

Was this topic helpful?