DecisionTree (JMSL Numerical Library)

java.lang.Object
- com.imsl.datamining.PredictiveModel
- - com.imsl.datamining.decisionTree.DecisionTree

All Implemented Interfaces:

Serializable, Cloneable

Direct Known Subclasses:

CHAID, DecisionTreeInfoGain, QUEST
```
public abstract class DecisionTree
extends PredictiveModel
implements Serializable, Cloneable
```
Abstract class for generating a decision tree for a single response variable and one or more predictor variables.

Tree Generation Methods

This package contains four of the most widely used algorithms for decision trees (C45, ALACART, CHAID, and QUEST). The user may also provide an alternate algorithm by extending the DecisionTree or DecisionTreeInfoGain abstract class and implementing the abstract method DecisionTree#selectSplitVariable( double[][], double[], double[], double[], int[]).

Optimal Tree Size

Minimum Cost-complexity Pruning

A strategy to address overfitting is to grow the tree as large as possible, and then use some logic to prune it back. Let T represent a decision tree generated by any of the methods above. The idea (from Breiman, et. al.) is to find the smallest sub-tree of T that minimizes the cost-complexity measure:

$R_{delta}(T)=R(T)+delta|tilde{T}|mbox{,}$

denotes the set of terminal nodes. represents the number of terminal nodes, and is a cost-complexity parameter. For a categorical target variable

$R(T)=sum_{tintilde{T}}{R(t)}= sum_{tintilde{T}}{r(t)p(t)}$

$r(t)=min_i{sum_j{C(i|j)p(j|t)}}$

and is the cost for misclassifying the actual class j as i. Note that and , for .

When the target is continuous (and the problem is a regression problem), the metric is instead the mean squared error

$R(T)=sum_{tin{tilde{T}}}R(t)=frac{1}{N} sum_{tin{tilde{T}}}sum_{y_nin{t}}(y_n-hat{y}(t))^2$

This class implements the optimal pruning algorithm 10.1, page 294 in Breiman, et. al (1984). The result of the algorithm is a sequence of sub-trees $T_{max}succ{T_1}succ{T_2}succ{T_{M-1}} succ{{t_0}}$ obtained by pruning the fully generated tree, $T_{max}$ , until the sub-tree consists of the single root node, ${t_0}$ . Corresponding to the sequence of sub-trees is the sequence of complexity values, $0leq delta_{min}ltdelta_1ltdelta_2ltldotsltdelta_{M-1}ltdelta_M$ where M is the number of steps it takes in the algorithm to reach the root node. The sub-trees represent the optimally-pruned sub-trees for the sequence of complexity values. The minimum complexity $delta_{min}$ can be set via an optional argument.

V-Fold Cross-Validation

The CrossValidation class can be used for model validation.

Prediction

Ensemble Methods

The BootstrapAggregation class provides predictions through an ensemble of fitted trees, where the training is done on bootstrap samples.

The GradientBoosting class provides predictions through an ensemble of trees trained on random subsets of the data and iteratively refined using the stochastic gradient boosting algorithm.

The RandomTrees class provides predictions through an ensemble of fitted trees, where the training is done on bootstrap samples and random subsets of predictors.

Missing Values

Any observation or case with a missing response variable is eliminated from the analysis. If a predictor has a missing value, each algorithm skips that case when evaluating the given predictor. When making a prediction for a new case, if the split variable is missing, the prediction function applies surrogate split-variables and splitting rules in turn, if they are estimated with the decision tree. Otherwise, the prediction function returns the prediction from the most recent non-terminal node. In this implementation, only ALACART estimates surrogate split variables when requested.

See Also:
Example 1, Example 2, Example 3, Example 4, Example 5, Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`DecisionTree.MaxTreeSizeExceededException` Exception thrown when the maximum tree size has been exceeded.
`static class`	`DecisionTree.PruningFailedToConvergeException` Exception thrown when pruning fails to converge.
`static class`	`DecisionTree.PureNodeException` Exception thrown when attempting to split a node that is already pure (response variable is constant).

Nested classes/interfaces inherited from class com.imsl.datamining.PredictiveModel
PredictiveModel.PredictiveModelException, PredictiveModel.StateChangeException, PredictiveModel.SumOfProbabilitiesNotOneException, PredictiveModel.VariableType

Constructor Summary

Constructors
Constructor and Description
`DecisionTree(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)` Constructs a `DecisionTree` object for a single response variable and multiple predictor variables.

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`fitModel()` Fits the decision tree.
`double[]`	`getCostComplexityValues()` Returns an array containing cost-complexity values.
`Tree`	`getDecisionTree()` Returns a `Tree` object.
`double`	`getFittedMeanSquaredError()` Returns the mean squared error on the training data.
`int`	`getMaxDepth()` Returns the maximum depth a tree is allowed to have.
`int`	`getMaxNodes()` Returns the maximum number of `TreeNode` instances allowed in a tree.
`double`	`getMeanSquaredPredictionError()` Returns the mean squared error.
`int`	`getMinObsPerChildNode()` Returns the minimum number of observations that are required for any child node before performing a split.
`int`	`getMinObsPerNode()` Returns the minimum number of observations that are required in a node before performing a split.
`int[]`	`getNodeAssigments(double[][] testData)` Returns the terminal node assignments for each row of the test data.
`int`	`getNumberOfComplexityValues()` Returns the number of cost complexity values determined by the pruning algorithm.
`int`	`getNumberOfRandomFeatures()` Returns the number of random features used in the splitting rules when `randomFeatureSelection`=`true`.
`boolean`	`isAutoPruningFlag()` Returns the current setting of the boolean to automatically prune the decision tree.
`boolean`	`isRandomFeatureSelection()` Returns the current setting of the boolean to perform random feature selection.
`double[]`	`predict()` Predicts the training examples (in-sample predictions) using the most recently grown tree.
`double[]`	`predict(double[][] testData)` Predicts new data using the most recently grown decision tree.
`double[]`	`predict(double[][] testData, double[] testDataWeights)` Predicts new weighted data using the most recently grown decision tree.
`void`	`printDecisionTree(boolean printMaxTree)` Prints the contents of the decision tree using distinct but general labels.
`void`	`printDecisionTree(String responseName, String[] predictorNames, String[] classNames, String[] categoryNames, boolean printMaxTree)` Prints the contents of the decision tree using labels.
`void`	`pruneTree(double gamma)` Finds the minimum cost-complexity decision tree for the cost-complexity value, gamma.
`protected abstract int`	`selectSplitVariable(double[][] xy, double[] classCounts, double[] parentFreq, double[] splitValue, double[] splitCriterionValue, int[] splitPartition)` Abstract method for selecting the next split variable and split definition for the node.
`void`	`setAutoPruningFlag(boolean autoPruningFlag)` Sets the flag to automatically prune the tree during the fitting procedure.
`protected void`	`setConfiguration(PredictiveModel pm)` Sets the configuration of `PredictiveModel` to that of the input model.
`void`	`setCostComplexityValues(double[] gammas)` Sets the cost-complexity values.
`void`	`setMaxDepth(int nLevels)` Sets the maximum tree depth allowed.
`void`	`setMaxNodes(int maxNodes)` Sets the maximum number of nodes allowed in a tree.
`void`	`setMinCostComplexityValue(double minCostComplexity)` Sets the value of the minimum cost-complexity value.
`void`	`setMinObsPerChildNode(int nObs)` Sets the minimum number of observations that a child node must have in order to split.
`void`	`setMinObsPerNode(int nObs)` Sets the minimum number of observations a node must have to allow a split.
`void`	`setNumberOfRandomFeatures(int numberOfRandomFeatures)` Sets the number of predictors in the random subset to select from at each node.
`void`	`setRandomFeatureSelection(boolean selectRandomFeatures)` Sets the flag to select split variables from a random subset of the features.

Methods inherited from class com.imsl.datamining.PredictiveModel
getClassCounts, getClassErrors, getClassLabels, getClassProbabilities, getCostMatrix, getMaxNumberOfCategories, getMaxNumberOfIterations, getNumberOfClasses, getNumberOfColumns, getNumberOfMissing, getNumberOfPredictors, getNumberOfRows, getNumberOfUniquePredictorValues, getPredictorIndexes, getPredictorTypes, getPrintLevel, getPriorProbabilities, getRandomObject, getResponseColumnIndex, getResponseVariableAverage, getResponseVariableMostFrequentClass, getResponseVariableType, getTotalWeight, getVariableType, getWeights, getXY, isMustFitModel, isUserFixedNClasses, setClassCounts, setClassLabels, setClassProbabilities, setCostMatrix, setMaxNumberOfCategories, setMaxNumberOfIterations, setMustFitModel, setNumberOfClasses, setPredictorIndex, setPredictorTypes, setPrintLevel, setPriorProbabilities, setRandomObject, setResponseColumnIndex, setTrainingData, setVariableType, setWeights

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - DecisionTree
```
public DecisionTree(double[][] xy,
            int responseColumnIndex,
            PredictiveModel.VariableType[] varType)
```
    Constructs a DecisionTree object for a single response variable and multiple predictor variables.
    
    Parameters:
    xy - a double matrix containing the training data and associated response values
    responseColumnIndex - an int specifying the column index of the response variable
    varType - a PredictiveModel.VariableType array containing the type of each variable
- Method Detail
  - fitModel
```
public void fitModel()
              throws PredictiveModel.PredictiveModelException,
                     DecisionTree.PruningFailedToConvergeException,
                     PredictiveModel.StateChangeException,
                     DecisionTree.PureNodeException,
                     PredictiveModel.SumOfProbabilitiesNotOneException,
                     DecisionTree.MaxTreeSizeExceededException
```
    Fits the decision tree. Implements the abstract method.
    
    Overrides:
    
    fitModel in class PredictiveModel
    
    Throws:
    
    PredictiveModel.PredictiveModelException - is thrown when an exception occurs in the com.imsl.datamining.PredictiveModel. Superclass exceptions should be considered such as com.imsl.datamining.PredictiveModel.StateChangeException and com.imsl.datamining.PredictiveModel.SumOfProbabilitiesNotOneException.
    
    DecisionTree.PruningFailedToConvergeException - is thrown when pruning fails to converge.
    
    PredictiveModel.StateChangeException - is thrown when an input parameter changes that might affect the model estimates or predictions.
    
    DecisionTree.PureNodeException - is thrown when attempting to split a node that is already pure.
    
    PredictiveModel.SumOfProbabilitiesNotOneException - is thrown when the sum of probabilities is not approximately one.
    
    DecisionTree.MaxTreeSizeExceededException - is thrown when the maximum tree size has been exceeded.
  - getCostComplexityValues
```
public double[] getCostComplexityValues()
                                 throws DecisionTree.PruningFailedToConvergeException,
                                        PredictiveModel.StateChangeException
```
    Returns an array containing cost-complexity values.
    
    Returns:
    a double array containing the cost-complexity values
    The cost-complexity values are found via the optimal pruning algorithm of Breiman, et. al.
    
    Throws:
    
    DecisionTree.PruningFailedToConvergeException - pruning has failed to converge.
    
    PredictiveModel.StateChangeException - an input parameter has changed that might affect the model estimates or predictions.
  - getDecisionTree
```
public Tree getDecisionTree()
                     throws PredictiveModel.StateChangeException
```
    Returns a Tree object.
    
    Returns:
    a Tree object containing the tree structure information
    
    Throws:
    
    PredictiveModel.StateChangeException - an input parameter has changed that might affect the model estimates or predictions.
  - getFittedMeanSquaredError
```
public double getFittedMeanSquaredError()
                                 throws PredictiveModel.StateChangeException
```
    Returns the mean squared error on the training data.
    
    Returns:
    a double equal to the mean squared error between the fitted value and the actual value of the response variable in the training data
    
    Throws:
    
    PredictiveModel.StateChangeException - an input parameter has changed that might affect the model estimates or predictions.
  - getMaxDepth
```
public int getMaxDepth()
```
    Returns the maximum depth a tree is allowed to have.
    
    Returns:
    an int, the maximum depth a tree is allowed to have
  - getMaxNodes
```
public int getMaxNodes()
```
    Returns the maximum number of TreeNode instances allowed in a tree.
    
    Returns:
    an int, the maximum number of nodes allowed in a tree
  - getMeanSquaredPredictionError
```
public double getMeanSquaredPredictionError()
                                     throws PredictiveModel.StateChangeException
```
    Returns the mean squared error.
    
    Returns:
    a double equal to the mean squared error between the predicted value and the actual value of the response variable. The error is the in-sample fitted error if predict is first called with no arguments. Otherwise, the error is relative to the test data provided in the call to predict.
    
    Throws:
    
    PredictiveModel.StateChangeException - an input parameter has changed that might affect the model estimates or predictions.
  - getMinObsPerChildNode
```
public int getMinObsPerChildNode()
```
    Returns the minimum number of observations that are required for any child node before performing a split.
    
    Returns:
    an int, the minimum number of observations that are required for any child node before performing a split
  - getMinObsPerNode
```
public int getMinObsPerNode()
```
    Returns the minimum number of observations that are required in a node before performing a split.
    
    Returns:
    an int indicating the minimum number of observations that are required in a node before performing a split.
  - getNodeAssigments
```
public int[] getNodeAssigments(double[][] testData)
```
    Returns the terminal node assignments for each row of the test data.
    
    Parameters:
    testData - a double matrix containing the test data
    testData must have the same column structure and type as the training data.
    
    Returns:
    an int array containing the (0-based) terminal node id's for each observation in testData
  - getNumberOfComplexityValues
```
public int getNumberOfComplexityValues()
```
    Returns the number of cost complexity values determined by the pruning algorithm.
    
    Returns:
    an int, the number of cost complexity values
  - getNumberOfRandomFeatures
```
public int getNumberOfRandomFeatures()
```
    Returns the number of random features used in the splitting rules when randomFeatureSelection=true.
    
    Returns:
    an int, the number of random features
  - isAutoPruningFlag
```
public boolean isAutoPruningFlag()
```
    Returns the current setting of the boolean to automatically prune the decision tree.
    
    Returns:
    a boolean, the value of the auto-pruning flag. If true, the model is configured to automatically prune the decision tree. If the flag is false, no pruning is performed.
  - isRandomFeatureSelection
```
public boolean isRandomFeatureSelection()
```
    Returns the current setting of the boolean to perform random feature selection.
    
    Returns:
    a boolean, the value of the flag. If true, the set of variables considered at each node is randomly selected. If the flag is false, all variables are considered at each node.
  - predict
```
public double[] predict()
                 throws PredictiveModel.StateChangeException
```
    Predicts the training examples (in-sample predictions) using the most recently grown tree.
    
    Specified by:
    
    predict in class PredictiveModel
    
    Returns:
    a double array containing fitted values of the response variable using the most recently grown decision tree. To populate fitted values, use the predict method without arguments.
    
    Throws:
    
    PredictiveModel.StateChangeException - is thrown when an input parameter changes that might affect the model estimates or predictions.
  - predict
```
public double[] predict(double[][] testData)
                 throws PredictiveModel.StateChangeException
```
    Predicts new data using the most recently grown decision tree.
    
    Specified by:
    
    predict in class PredictiveModel
    
    Parameters:
    testData - a double matrix containing test data for which predictions are to be made using the current tree. testData must have the same number of columns in the same arrangement as xy.
    
    Returns:
    a double array containing predicted values
    
    Throws:
    
    PredictiveModel.StateChangeException - is thrown when an input parameter changes that might affect the model estimates or predictions.
  - predict
```
public double[] predict(double[][] testData,
               double[] testDataWeights)
                 throws PredictiveModel.StateChangeException
```
    Predicts new weighted data using the most recently grown decision tree.
    
    Specified by:
    
    predict in class PredictiveModel
    
    Parameters:
    testData - a double matrix containing test data for which predictions are to be made using the current tree. testData must have the same number of columns in the same arrangement as xy.
    testDataWeights - a double array containing weights for each row of testData
    
    Returns:
    a double array containing predicted values
    
    Throws:
    
    PredictiveModel.StateChangeException - is thrown when an input parameter changes that might affect the model estimates or predictions.
  - printDecisionTree
```
public void printDecisionTree(boolean printMaxTree)
```
    Prints the contents of the decision tree using distinct but general labels.
    This method uses default values for the variable labels when printing (see printDecisionTree (String, String[], String[], String[], boolean) for these values.)
    
    Parameters:
    printMaxTree - a boolean indicating that the maximal tree should be printed. When true, the maximal tree is printed. Otherwise, the pruned tree is printed.
  - printDecisionTree
```
public void printDecisionTree(String responseName,
                     String[] predictorNames,
                     String[] classNames,
                     String[] categoryNames,
                     boolean printMaxTree)
```
    Prints the contents of the decision tree using labels.
    
    Parameters:
    responseName - a String specifying a name for the response variable
    If null, the default value is used.
    
    Default: responseName = Y
    predictorNames - a String array specifying names for the predictor variables
    If null, the default value is used.
    
    Default: predictorNames = X0, X1, ...
    classNames - a String array specifying names for the class levels
    If null, the default value is used.
    
    Default: classNames = 0, 1, 2, ...
    categoryNames - a String array specifying names for the categories of the predictor variables
    If null, the default value is used.
    
    Default: categoryNames = 0, 1, 2, ...
    printMaxTree - a boolean indicating that the maximal tree should be printed. When true, the maximal tree is printed. Otherwise, the pruned tree is printed.
  - pruneTree
```
public void pruneTree(double gamma)
```
    Finds the minimum cost-complexity decision tree for the cost-complexity value, gamma.
    The method implements the optimal pruning algorithm 10.1, page 294 in Breiman, et. al (1984). The result of the algorithm is a sequence of sub-trees $T_{max}succ{T_1}succ{T_2}succ{T_{M-1}} succ{{t_0}}$ obtained by pruning the fully generated tree, $T_{max}$ , until the sub-tree consists of the single root node, ${t_0}$ . Corresponding to the sequence of sub-trees is the sequence of complexity values, $0leq delta_{min}ltdelta_1ltdelta_2ltldotsltdelta_{M-1}ltdelta_M$ where M is the number of steps it takes in the algorithm to reach the root node. The sub-trees represent the optimally pruned sub-trees for the sequence of complexity values.
    
    The effect of the pruning is stored in the tree's terminal node array. That is, when the algorithm determines that the tree should be pruned at a particular node, it sets that node to be a terminal node using the method Tree.setTerminalNode(int, boolean).
    
    No other changes are made to the tree structure so that the maximal tree can still be printed and reviewed. However, once a tree is pruned, all the predictions will use the pruned tree.
    
    Parameters:
    gamma - a double giving the value of the cost-complexity parameter
  - selectSplitVariable
```
protected abstract int selectSplitVariable(double[][] xy,
                      double[] classCounts,
                      double[] parentFreq,
                      double[] splitValue,
                      double[] splitCriterionValue,
                      int[] splitPartition)
```
    Abstract method for selecting the next split variable and split definition for the node.
    
    Parameters:
    xy - a double matrix containing the data
    classCounts - a double array containing the counts for each class of the response variable, when it is categorical
    parentFreq - a double array used to indicate which subset of the observations belong in the current node
    splitValue - a double array representing the resulting split point if the selected variable is quantitative
    splitCriterionValue - a double, the value of the criterion used to determine the splitting variable
    splitPartition - an int array indicating the resulting split partition if the selected variable is categorical
    
    Returns:
    an int specifying the index of the split variable in this.getPredictorIndexes()
  - setAutoPruningFlag
```
public void setAutoPruningFlag(boolean autoPruningFlag)
```
    Sets the flag to automatically prune the tree during the fitting procedure.
    The default value is false. Set to true before calling fitModel() in order to prune the tree automatically. The pruning will use the cost-complexity value equal to minCostComplexityValue. See also pruneTree(double) which prunes the tree using a given cost-complexity value.
    
    Parameters:
    autoPruningFlag - a boolean, specifying the value of the flag. If true, the maximally grown tree should be automatically pruned in fitModel()
    Default: autoPruningFlag=false.
  - setConfiguration
```
protected void setConfiguration(PredictiveModel pm)
                         throws DecisionTree.PruningFailedToConvergeException,
                                PredictiveModel.StateChangeException,
                                PredictiveModel.SumOfProbabilitiesNotOneException
```
    Sets the configuration of PredictiveModel to that of the input model.
    
    Specified by:
    
    setConfiguration in class PredictiveModel
    
    Parameters:
    pm - a PredictiveModel object
    
    Throws:
    
    DecisionTree.PruningFailedToConvergeException - is thrown when pruning fails to converge.
    
    PredictiveModel.StateChangeException - is thrown when an input parameter has changed that might affect the model estimates or predictions.
    
    PredictiveModel.SumOfProbabilitiesNotOneException - is thrown when the sum of the probabilities does not equal 1.
  - setCostComplexityValues
```
public void setCostComplexityValues(double[] gammas)
```
    Sets the cost-complexity values. For the original tree, the values are generated in fitModel() when isAutoPruningFlag() returns true.
    
    Parameters:
    gammas - double array containing cost-complexity values. This method is used when copying the configuration of one tree to another.
    Default: gammas = setMinCostComplexityValue(double).
  - setMaxDepth
```
public void setMaxDepth(int nLevels)
```
    Sets the maximum tree depth allowed.
    
    Parameters:
    nLevels - an int specifying the maximum depth that the DecisionTree is allowed to have. nLevels should be strictly positive.
    Default: nLevels = 10.
  - setMaxNodes
```
public void setMaxNodes(int maxNodes)
```
    Sets the maximum number of nodes allowed in a tree.
    
    Parameters:
    maxNodes - an int specifying the maximum number of nodes allowed in a tree
    Default: maxNodes = 100.
  - setMinCostComplexityValue
```
public void setMinCostComplexityValue(double minCostComplexity)
```
    Sets the value of the minimum cost-complexity value.
    
    Parameters:
    minCostComplexity - a double indicating the smallest value to use in cost-complexity pruning. The value must be in [0.0, 1.0].
    Default: minCostComplexity = 0.
  - setMinObsPerChildNode
```
public void setMinObsPerChildNode(int nObs)
```
    Sets the minimum number of observations that a child node must have in order to split.
    
    Parameters:
    nObs - an int specifying the minimum number of observations that a child node must have in order to split the current node. nObs must be strictly positive. nObs must also be greater than the minimum number of observations required before a node can split setMinObsPerNode(int).
    Default: nObs = 7.
  - setMinObsPerNode
```
public void setMinObsPerNode(int nObs)
```
    Sets the minimum number of observations a node must have to allow a split.
    
    Parameters:
    nObs - an int specifying the number of observations the current node must have before considering a split. nObs should be greater than 1 but less than or equal to the number of observations in xy.
    Default: nObs = 21.
  - setNumberOfRandomFeatures
```
public void setNumberOfRandomFeatures(int numberOfRandomFeatures)
```
    Sets the number of predictors in the random subset to select from at each node.
    
    Parameters:
    numberOfRandomFeatures - an int, the number of predictors in the random subset
    Default: numberOfFeatures= for classification problems, and p/3 for regression problems, where p is the number of predictors in the training data
  - setRandomFeatureSelection
```
public void setRandomFeatureSelection(boolean selectRandomFeatures)
```
    Sets the flag to select split variables from a random subset of the features.
    
    Parameters:
    selectRandomFeatures - a boolean, indicating whether or not to select random features
    Default: selectRandomFeaturesfalse

Class DecisionTree

Tree Generation Methods

Optimal Tree Size

Minimum Cost-complexity Pruning

V-Fold Cross-Validation

Prediction

Ensemble Methods

Missing Values

Nested Class Summary

Nested classes/interfaces inherited from class com.imsl.datamining.PredictiveModel

Constructor Summary

Method Summary

Methods inherited from class com.imsl.datamining.PredictiveModel

Methods inherited from class java.lang.Object

Constructor Detail

DecisionTree

Method Detail

fitModel

getCostComplexityValues

getDecisionTree

getFittedMeanSquaredError

getMaxDepth

getMaxNodes

getMeanSquaredPredictionError

getMinObsPerChildNode

getMinObsPerNode

getNodeAssigments

getNumberOfComplexityValues

getNumberOfRandomFeatures

isAutoPruningFlag

isRandomFeatureSelection

predict

predict

predict

printDecisionTree

printDecisionTree

pruneTree

selectSplitVariable

setAutoPruningFlag

setConfiguration

setCostComplexityValues

setMaxDepth

setMaxNodes

setMinCostComplexityValue

setMinObsPerChildNode

setMinObsPerNode

setNumberOfRandomFeatures

setRandomFeatureSelection