CompactRegressionTree

Compact regression tree

Description

Compact version of a regression tree (of class RegressionTree). The compact version does not include the data for training the regression tree. Therefore, you cannot perform some tasks with a compact regression tree, such as cross validation. Use a compact regression tree for making predictions (regressions) of new data.

Creation

Create a CompactRegressionTree object from a full RegressionTree model object by using compact.

Properties

CategoricalPredictors — Indices of categorical predictors

vector of positive integers | []

This property is read-only.

Categorical predictor indices, specified as a vector of positive integers. CategoricalPredictors contains index values indicating that the corresponding predictors are categorical. The index values are between 1 and p, where p is the number of predictors used to train the model. If none of the predictors are categorical, then this property is empty ([]).

Data Types: single | double

CategoricalSplit — Categorical splits

n-by-2 cell array

This property is read-only.

Categorical splits, returned as an n-by-2 cell array, where n is the number of categorical splits in tree. Each row in CategoricalSplit 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 CategoricalSplit(j,1) and the right child is chosen if z is in CategoricalSplit(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.

Data Types: cell

Children — Numbers of the child nodes for each node

n-by-2 array

This property is read-only.

Numbers of the child nodes for each node in tree, returned as an n-by-2 array containing the numbers of the child nodes for each node in , where n is the number of nodes. Leaf nodes have child node 0.

Data Types: double

CutCategories — Categories used at branches

n-by-2 cell array

This property is read-only.

Categories used at branches in tree, returned as an n-by-2 cell array, 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.

Data Types: cell

CutPoint — Values used as cut points

n-element vector

This property is read-only.

Values used as cut points in tree, returned as an n-element vector, 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.

Data Types: double

CutPredictor — Names of the variables used for branching in each node

cell array

This property is read-only.

Names of the variables used for branching in each node in tree, returned as an n-element cell array, 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.

Data Types: cell

CutPredictorIndex — Indices of variables used for branching in each node

n-element array

This property is read-only.

Indices of variables used for branching in each node in tree, returned as an n-element array, where n is the number of nodes. For more information, see CutPredictor.

Data Types: double

CutType — Type of cut at each node

n-element cell array

This property is read-only.

Type of cut at each node in tree, returned as an n-element cell array, 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.

Data Types: cell

ExpandedPredictorNames — Expanded predictor names

cell array of character vectors

This property is read-only.

Expanded predictor names, returned 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.

Data Types: cell

IsBranchNode — Indicator of branch nodes

logical vector

This property is read-only.

Indicator of branch nodes, returned as an n-element logical vector that is true for each branch node and false for each leaf node of tree.

Data Types: logical

NodeError — Mean squared error for each node

n-element vector

This property is read-only.

Mean squared error for each node in tree, returned as an n-element vector, where n is the number of nodes in the tree.

Data Types: double

NodeMean — Mean observation values for each node

n-element vector

This property is read-only.

Mean observation values for each node in tree, returned as an n-element vector, 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.

Data Types: double

NodeProbability — Proportion of observations in original data that satisfy the conditions for the node

n-element vector

This property is read-only.

Proportion of observations in original data that satisfy the conditions for each node in tree, returned as an n-element vector, where n is the number of nodes in the tree.

Data Types: double

NodeRisk — Risk of each node

n-element vector

This property is read-only.

Risk of each node in tree, returned as an n-element vector, where n is the number of nodes in the tree. The risk for each node is the node error weighted by the node probability.

Data Types: double

NodeSize — Size of nodes

n-element vector

This property is read-only.

Size of the nodes in tree, returned as an n-element vector, where n is the number of nodes in the tree. The size of a node is the number of observations from the data used to create the tree that satisfy the conditions for the node.

Data Types: double

NumNodes — Number of nodes

positive integer

This property is read-only.

The number of nodes in tree, returned as a positive integer.

Data Types: double

Parent — Number of parents of nodes

n-element vector

This property is read-only.

Number of parents of each node in tree, returned as an n-element integer vector, where n is the number of nodes in the tree. The parent of the root node is 0.

Data Types: double

PredictorNames — Predictor names

cell array of character vectors

This property is read-only.

Predictor names, specified as a cell array of character vectors. The order of the entries in PredictorNames is the same as in the training data.

Data Types: cell

PruneAlpha — Alpha values for pruning the tree

real vector

Alpha values for pruning the tree, returned as a real 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.

For the meaning of the ɑ values, see How Decision Trees Create a Pruning Sequence.

Data Types: double

PruneList — Pruning levels of each node in tree

integer vector

Pruning levels of each node in the tree, returned as an integer vector with NumNodes elements. The pruning levels range from 0 (no pruning) to M, where M is the distance between the deepest leaf and the root node.

For details, see Pruning.

Data Types: double

ResponseName — Name of the response variable

character vector

This property is read-only.

Name of the response variable, returned as a character vector.

Data Types: char

ResponseTransform — Function for transforming responses

'none' (default) | function handle

Function for transforming the raw response values (mean squared error), specified as a function handle or 'none'. The default 'none' means no transformation; equivalently, 'none' means @(x)x. A function handle must accept a matrix of response values and return a matrix of the same size.

Add or change a ResponseTransform function using dot notation:

tree.ResponseTransform = @function

Data Types: char | function_handle

SurrogateCutCategories — Categories used for surrogate splits

n-element cell array

This property is read-only.

Categories used for surrogate splits, returned as an n-element cell array, 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 two-element cell array with categories for a categorical surrogate predictor. The first element of this two-element cell array lists categories assigned to the left child by this surrogate split and the second element of this two-element 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 SurrogateCutVar. The optimal-split variable at this node does not appear. For nonbranch (leaf) nodes, SurrogateCutCategories contains an empty cell.

Data Types: cell

SurrogateCutFlip — Numeric cut assignments used for surrogate splits

n-element cell array

This property is read-only.

Numeric cut assignments used for surrogate splits in tree, returned as an n-element cell array, 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 optimal-split variable at this node does not appear. For nonbranch (leaf) nodes, SurrogateCutFlip contains an empty array.

Data Types: cell

SurrogateCutPoint — Numeric values used for surrogate splits

n-element cell array

This property is read-only.

Numeric values used for surrogate splits in tree, returned as an n-element cell array, 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 optimal-split variable at this node does not appear. For nonbranch (leaf) nodes, SurrogateCutPoint contains an empty cell.

Data Types: cell

SurrogateCutPredictor — Names of variables used for surrogate splits in each node

n-element cell array

This property is read-only.

Names of the variables used for surrogate splits in each node in tree, returned as an n-element cell array, 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 optimal-split variable at this node does not appear. For nonbranch (leaf) nodes, SurrogateCutPredictor contains an empty cell.

Data Types: cell

SurrogateCutType — Types of surrogate splits at each node

n-element cell array

This property is read-only.

Types of surrogate splits at each node in tree, returned as an n-element cell array, 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 optimal-split 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.

Data Types: cell

SurrogatePredictorAssociation — Predictive measures of association for surrogate splits

n-element cell array

This property is read-only.

Predictive measures of association for surrogate splits in tree, returned as an n-element cell array, 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 optimal-split variable at this node does not appear. For nonbranch (leaf) nodes, SurrogatePredictorAssociation contains an empty cell.

Data Types: cell

Object Functions

gather	Gather properties of Statistics and Machine Learning Toolbox object from GPU
lime	Local interpretable model-agnostic explanations (LIME)
loss	Regression error for regression tree model
nodeVariableRange	Retrieve variable range of decision tree node
partialDependence	Compute partial dependence
plotPartialDependence	Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots
predict	Predict responses using regression tree model
predictorImportance	Estimates of predictor importance for regression tree
shapley	Shapley values
surrogateAssociation	Mean predictive measure of association for surrogate splits in regression tree
update	Update model parameters for code generation
view	View regression tree

Examples

Construct and Compact a Regression Tree

Load the sample data.

load carsmall

Construct a regression tree for the sample data.

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

Make a compact version of the tree.

ctree = compact(tree);

Compare the size of the compact tree to that of the full tree.

t = whos('tree'); % t.bytes = size of tree in bytes
c = whos('ctree'); % c.bytes = size of ctree in bytes
[c.bytes t.bytes]

ans = 1×2

        4480        7727

The compact tree is smaller than the full tree.

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

• The predict and update functions support code generation.

• To integrate the prediction of a regression tree model into Simulink^®, you can use the RegressionTree Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB^® Function block with the predict function.

• When you train a regression tree model by using fitrtree, the following restrictions apply.

  • The value of the ResponseTransform name-value argument cannot be an anonymous function. For fixed-point code generation, the value must be 'none' (default).

  • You cannot use surrogate splits; that is, the value of the Surrogate name-value argument must be 'off'.

  • Fixed-point code generation and code generation with a coder configurer do not support categorical predictors (logical, categorical, char, string, or cell). You cannot use the CategoricalPredictors name-value argument. To include categorical predictors in a model, preprocess them by using dummyvar before fitting the model.

For more information, see Introduction to Code Generation.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• The following object functions fully support GPU arrays:

  • gather

  • nodeVariableRange

  • predictorImportance

  • surrogateAssociation

• The following object functions offer limited support for GPU arrays:

  • loss

  • partialDependence

  • plotPartialDependence

  • predict

  • view

• The object functions execute on a GPU if any of the following apply:

  • The model was fitted with GPU arrays.

  • The predictor data that you pass to the object function is a GPU array.

  • The response data that you pass to the object function is a GPU array.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2011a