nodeVariableRange

Retrieve variable range of decision tree node

Since R2020a

Syntax

varRange = nodeVariableRange(tree,nodeID)

varRange = nodeVariableRange(tree,nodeID,OmitUnusedVariables=omitUnusedVars)

Description

varRange = nodeVariableRange(tree,nodeID) returns the range of predictor variables varRange at the tree node specified by nodeID.

varRange = nodeVariableRange(tree,nodeID,OmitUnusedVariables=omitUnusedVars) also specifies whether to omit the unused predictor variables from the returned varRange.

Examples

Variable Range of Decision Tree Node

Create a decision tree for classification, and retrieve the range of variables at a specified node of the decision tree.

Load the census1994 data set. The table adultdata contains six numeric and eight categorical variables.

load census1994

Train a classification tree based on the features contained in adultdata and the class labels in adultdata.salary. Limit the number of splits in the tree by specifying the name-value argument MaxNumSplits.

tree = fitctree(adultdata,"salary",MaxNumSplits=31)

tree = 
  ClassificationTree
           PredictorNames: {'age'  'workClass'  'fnlwgt'  'education'  'education_num'  'marital_status'  'occupation'  'relationship'  'race'  'sex'  'capital_gain'  'capital_loss'  'hours_per_week'  'native_country'}
             ResponseName: 'salary'
    CategoricalPredictors: [2 4 6 7 8 9 10 14]
               ClassNames: [<=50K    >50K]
           ScoreTransform: 'none'
          NumObservations: 32561


  Properties, Methods

tree is a trained ClassificationTree model for classification.

View the graphical display of the trained classification tree.

view(tree,Mode="graph")

Retrieve the range of predictor variables at node 10.

varRange = nodeVariableRange(tree,10)

varRange = struct with fields:
             age: [-Inf 20.5000]
    relationship: [Not-in-family    Other-relative    Own-child    Unmarried]
    capital_gain: [7.0735e+03 Inf]

Input Arguments

tree — Decision tree model

ClassificationTree object | CompactClassificationTree object | RegressionTree object | CompactRegressionTree object

Decision tree model, specified as one of the following:

• ClassificationTree object returned by fitctree

• CompactClassificationTree object returned by the ClassificationTree object function compact

• RegressionTree object returned by fitrtree

• CompactRegressionTree object returned by the RegressionTree object function compact

nodeID — Node in decision tree

positive integer scalar

Node in the decision tree, specified as a positive integer scalar. nodeID must be less than or equal to the number of nodes in the decision tree.

Data Types: single | double

omitUnusedVars — Indicator to omit unused predictor variables

true or 1 (default) | false or 0

Indicator to omit unused predictor variables from varRange, specified as a numeric or logical 1 (true) or 0 (false).

Output Arguments

varRange — Variable range at decision tree node

structure

Variable range at the decision tree node, returned as a structure. If a predictor variable is numeric, the corresponding field of varRange is a 1-by-2 numeric vector containing the lower and upper bounds. If a predictor variable is categorical, the corresponding field of varRange is a categorical array containing the categories subgroup.

Extended Capabilities

GPU Arrays

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

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2020a