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. 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.

example

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

Examples

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Variable Range of Decision Tree Node

Open Live Script

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

tree is a trained ClassificationTree model for classification.

View the graphical display of the trained classification tree.

view(tree,Mode="graph")

Figure Classification tree viewer contains an axes object and other objects of type uimenu, uicontrol. The axes object contains 84 objects of type line, text. One or more of the lines displays its values using only markers

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

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`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).

Data Types: logical

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