PredictiveModel (JMSL Numerical Library (jmsl) 2021.0.0 API)

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

All Implemented Interfaces:

Serializable, Cloneable

Direct Known Subclasses:

DecisionTree, GradientBoosting, RandomTrees, SupportVectorMachine
```
public abstract class PredictiveModel
extends Object
implements Serializable, Cloneable
```
Specifies a predictive model. This class defines the members and methods common to predictive models in univariate prediction or classification problems.

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`PredictiveModel.CloneNotSupportedException` Wraps the `java.lang.CloneNotSupportedException` to indicate that the `clone` method in class `Object` has been called to clone an object, but that the object's class does not implement the `Cloneable` interface.
`static class`	`PredictiveModel.PredictiveModelException` An exception class intended to be the parent of all nested Exception classes where the enclosing class extends `PredictiveModel`.
`static class`	`PredictiveModel.StateChangeException` Exception thrown when an input parameter has changed that might affect the model estimates or predictions.
`static class`	`PredictiveModel.SumOfProbabilitiesNotOneException` Exception thrown when the sum of probabilities is not approximately one.
`static class`	`PredictiveModel.VariableType` Enumerates different variable types.

Constructor Summary

Constructors
Modifier	Constructor and Description
`protected`	`PredictiveModel(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)` Constructs a `PredictiveModel` object for a single response variable and multiple predictor variables.
`protected`	`PredictiveModel(PredictiveModel pm)` Constructs a `PredictiveModel` from an existing instance.

Method Summary

All Methods Instance Methods Abstract Methods Concrete Methods
Modifier and Type	Method and Description
`abstract PredictiveModel`	`clone()` Abstract clone method.
`void`	`fitModel()` Fits the predictive model to the training data (estimates the model using the training data and current configuration settings).
`double[]`	`getClassCounts()` Returns the counts of each class (level) of the categorical response variable.
`int[][]`	`getClassErrors(double[] knownValues, double[] predictedValues)` Returns classification error information.
`String[]`	`getClassLabels()` Returns the current class labels for a categorical response variable.
`double[][]`	`getClassProbabilities()` Returns a matrix containing the predicted class probabilities for each observation in the training data
`double[][]`	`getCostMatrix()` Returns the cost matrix for a categorical response variable.
`int`	`getMaxNumberOfCategories()` Returns the maximum number of categories allowed.
`int`	`getMaxNumberOfIterations()` Returns the maximum number of iterations allowed for the fitting procedure or training algorithm.
`int`	`getNumberOfClasses()` Returns the number of unique classes found in the categorical response data.
`int`	`getNumberOfColumns()` Returns the number of columns in the training data `xy`.
`int`	`getNumberOfMissing()` Returns the number of missing values of the response variable found in the data `xy`.
`int`	`getNumberOfPredictors()` Returns the number of predictors.
`int`	`getNumberOfRows()` Returns the number of rows in `xy` (observations).
`int[]`	`getNumberOfUniquePredictorValues()` Returns an array containing the number of distinct values of each predictor found in the input data.
`int[]`	`getPredictorIndexes()` Returns the column indices of `xy` in which the predictor variables reside.
`PredictiveModel.VariableType[]`	`getPredictorTypes()` Returns an array of `VariableType` objects that correspond to the predictor data types in `xy`.
`int`	`getPrintLevel()` Returns the current print level.
`double[]`	`getPriorProbabilities()` Returns an array containing the prior probabilities.
`Random`	`getRandomObject()` Returns the random object being used in the permutation of the observations.
`int`	`getResponseColumnIndex()` Returns the column index in `xy` containing the response variable.
`double`	`getResponseVariableAverage()` Returns the weighted average value of the response variable.
`int`	`getResponseVariableMostFrequentClass()` Returns the most frequent value of the response variable.
`PredictiveModel.VariableType`	`getResponseVariableType()` Returns the variable type of the response variable.
`double`	`getTotalWeight()` Returns the sum of the active case weights.
`PredictiveModel.VariableType[]`	`getVariableType()` Returns an array containing the variable types in `xy`.
`double[]`	`getWeights()` Returns an array containing the case weights.
`double[][]`	`getXY()` Returns a copy of the `xy` data.
`boolean`	`isConstantSeries()` Returns the current value of the `constantSeries` flag.
`boolean`	`isMustFitModel()` Returns the current value of the `mustFitModel` flag.
`boolean`	`isUserFixedNClasses()` Returns `true` if the number of classes was fixed by the user.
`abstract double[]`	`predict()` Predicts the response variable using the most recent fit.
`abstract double[]`	`predict(double[][] testData)` Predicts the response values using the most recent fit and the provided test data.
`abstract double[]`	`predict(double[][] testData, double[] testDataWeights)` Predicts the response values using the most recent fit, the provided test data, and the test data case weights.
`void`	`setClassCounts(double[] classCounts)` Sets the counts of each class of the response variable.
`void`	`setClassLabels(String[] classLabels)` Sets the class names or labels for a categorical response variable.
`void`	`setClassProbabilities(double[][] probs)` Sets the class probabilities.
`protected abstract void`	`setConfiguration(PredictiveModel pm)` Sets the configuration of `PredictiveModel` to that of the input model.
`void`	`setCostMatrix(double[][] costMatrix)` Specifies the cost matrix for a categorical response variable.
`void`	`setMaxNumberOfCategories(int maxCategories)` Sets the maximum number of categories allowed within categorical predictor and response variables.
`void`	`setMaxNumberOfIterations(int maxIterations)` Sets the maximum number of iterations allowed for the fitting procedure or training algorithm.
`void`	`setMustFitModel(boolean mustFitModel)` Sets the flag of whether or not the model needs to be fit or re-estimated because of a change in the data or configuration.
`void`	`setNumberOfClasses(int nClasses)` Sets the number of distinct classes of the response variable.
`void`	`setPredictorIndex(int[] predIdx)` Sets the column indices of `xy` in which the predictor variables reside.
`void`	`setPredictorTypes(PredictiveModel.VariableType[] predVarType)` Sets the `VariableType` objects that correspond to the predictor data types in `xy`.
`void`	`setPrintLevel(int printLevel)` Sets the print level for a `PredictiveModel`.
`void`	`setPriorProbabilities(double[] priors)` Sets the prior probabilities for class membership.
`void`	`setRandomObject(Random r)` Sets the random object to be used in the permutation of observation data.
`void`	`setResponseColumnIndex(int index)` Sets the column index in `xy` containing the response variable.
`void`	`setTrainingData(double[][] xy, int responseColumnIndex, PredictiveModel.VariableType[] varType)` Sets up the training data for the predictive model.
`void`	`setVariableType(PredictiveModel.VariableType[] varType)` Sets the variable types for the data.
`void`	`setWeights(double[] weights)` Specifies the case weights.

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

- Constructor Detail
  - PredictiveModel
```
protected PredictiveModel(PredictiveModel pm)
```
    Constructs a PredictiveModel from an existing instance.
    
    Parameters:
    
    pm - an instance of a PredictiveModel
  - PredictiveModel
```
protected PredictiveModel(double[][] xy,
                          int responseColumnIndex,
                          PredictiveModel.VariableType[] varType)
```
    Constructs a PredictiveModel object for a single response variable and multiple predictor variables.
    This constructor should be called by all classes extending PredictiveModel.
    
    Parameters:
    
    xy - a double matrix containing the training data and associated response values
    
    responseColumnIndex - an int specifying the column index in xy of the response variable
    
    varType - a PredictiveModel.VariableType array of length equal to xy[0].length containing the type of each variable
- Method Detail
  - clone
```
public abstract PredictiveModel clone()
```
    Abstract clone method. Each instance of a PredictiveModel must override this method.
    
    Overrides:
    
    clone in class Object
    
    Returns:
    
    a clone object of a specific PredictiveModel
  - setConfiguration
```
protected abstract void setConfiguration(PredictiveModel pm)
                                  throws PredictiveModel.PredictiveModelException
```
    Sets the configuration of PredictiveModel to that of the input model.
    Each instance of a PredictiveModel must override this method. The implementation should use specific class methods to set the parameter settings to that of the input PredictiveModel instance, essentially creating a copy of the input model. This method is used for model parameter tuning such as is done in CrossValidation, where several variations of the same model are evaluated and in ensemble methods, such as BootstrapAggregation, where several identical instances are fit to random samples.
    
    Each PredictiveModel subclass must override this method.
    
    Parameters:
    
    pm - a PredictiveModel object
    
    Throws:
    
    PredictiveModel.PredictiveModelException - is thrown when exceptions occur in the enclosing class that extends PredictiveModel.
  - predict
```
public abstract double[] predict()
                          throws PredictiveModel.PredictiveModelException
```
    Predicts the response variable using the most recent fit.
    Each PredictiveModel subclass must override this method.
    
    Returns:
    
    a double array containing the predicted values.
    
    Throws:
    
    PredictiveModel.PredictiveModelException - is thrown when an exception occurs in the common PredictiveModel methods. Implementing or overriding methods from this class may require throwing exceptions. Exceptions thrown from these methods will necessarily extend the PredictiveModelException.
  - predict
```
public abstract double[] predict(double[][] testData)
                          throws PredictiveModel.PredictiveModelException
```
    Predicts the response values using the most recent fit and the provided test data.
    Each PredictiveModel subclass must override this method.
    
    Parameters:
    
    testData - a double matrix containing data to be predicted. testData must have the same number of columns in the same arrangement as xy (the observations).
    
    Returns:
    
    a double array containing the predicted values.
    
    Throws:
    
    PredictiveModel.PredictiveModelException - is thrown when an exception occurs in the common PredictiveModel methods. Implementing or overriding methods from this class may require throwing exceptions. Exceptions thrown from these methods will necessarily extend the PredictiveModelException.
  - predict
```
public abstract double[] predict(double[][] testData,
                                 double[] testDataWeights)
                          throws PredictiveModel.PredictiveModelException
```
    Predicts the response values using the most recent fit, the provided test data, and the test data case weights.
    Each PredictiveModel subclass must override this method.
    
    Parameters:
    
    testData - double matrix containing data to be predicted. testData must have the same number of columns in the same arrangement as xy (the observations).
    
    testDataWeights - a double array containing weights for each row of testData.
    
    Returns:
    
    a double array containing the predicted values.
    
    Throws:
    
    PredictiveModel.PredictiveModelException - is thrown when an exception occurs in the common PredictiveModel methods. Implementing or overriding methods from this class may require throwing exceptions. Exceptions thrown from these methods will necessarily extend the PredictiveModelException.
  - fitModel
```
public void fitModel()
              throws PredictiveModel.PredictiveModelException
```
    Fits the predictive model to the training data (estimates the model using the training data and current configuration settings).
    Subclasses of PredictiveModel, such as DecisionTrees, override this method with specific model fitting algorithms.
    
    Throws:
    
    PredictiveModel.PredictiveModelException - is thrown when an exception occurs in the common PredictiveModel methods. Implementing or overriding methods from this class may require throwing exceptions. Exceptions thrown from these methods will necessarily extend the PredictiveModelException.
  - getClassErrors
```
public int[][] getClassErrors(double[] knownValues,
                              double[] predictedValues)
```
    Returns classification error information.
    
    Parameters:
    
    knownValues - a double array containing the known target classifications
    
    predictedValues - a double array containing the predicted classifications
    Arrays knownValues and predictedValues must be the same length.
    
    Returns:
    
    An int matrix of size (nClasses+1) by 2 containing the number of classification errors and the number of non-missing classifications for each target classification, plus the overall totals for these errors.
    For \(i \lt \, \)nClasses, the i-th row contains the number of classification errors for the i-th class and the number of patterns with non-missing classifications for that class. The last row contains the number of classification errors totaled over all target classifications, and the total number of patterns with non-missing target classifications.
  - getClassCounts
```
public double[] getClassCounts()
```
    Returns the counts of each class (level) of the categorical response variable.
    If the response variable is not PredictiveModel.VariableType.CATEGORICAL nor PredictiveModel.VariableType.ORDERED_DISCRETE, null is returned.
    
    Returns:
    
    a double array containing the summation of the case weights for each occurrence of a particular class found in the categorical response data.
  - setClassCounts
```
public void setClassCounts(double[] classCounts)
```
    Sets the counts of each class of the response variable.
    Use this method to set the class counts, when one or more classes do not occur in the training data due to sampling, but are otherwise valid, or when the data is distributed and the global counts are available. Only applies when the response variable is of type PredictiveModel.VariableType.CATEGORICAL or PredictiveModel.VariableType.ORDERED_DISCRETE.
    
    Parameters:
    
    classCounts - a double array containing the class counts of the response variable
    The default is to use the class counts discovered in the input matrix, xy, weighted by the values in weights.
  - setClassLabels
```
public void setClassLabels(String[] classLabels)
```
    Sets the class names or labels for a categorical response variable.
    
    Parameters:
    
    classLabels - a string array containing class names or labels. The array classLabels must have length = nClasses.
    Default: classLabels = {"1", "2", ...,"K"}, where K = nClasses
  - getClassLabels
```
public String[] getClassLabels()
```
    Returns the current class labels for a categorical response variable.
    Note: The labels will be null unless they have been set using the method setClassLabels.
    
    Returns:
    
    a string array containing the labels for each class level
  - setMustFitModel
```
public void setMustFitModel(boolean mustFitModel)
```
    Sets the flag of whether or not the model needs to be fit or re-estimated because of a change in the data or configuration.
    
    Parameters:
    
    mustFitModel - a boolean giving the value of the flag
    Default: mustFitModel=true.
  - getCostMatrix
```
public double[][] getCostMatrix()
```
    Returns the cost matrix for a categorical response variable.
    The cost matrix has elements C(i, j) = cost of misclassifying a response in class j as in class i. The diagonal elements of the cost matrix must be 0. In the case that nClasses has not been determined (usually because PredictiveModel.fitModel() has not been called), an array of length zero is returned.
    
    Returns:
    
    a square double matrix of dimension nClasses by nClasses containing the cost matrix for a categorical response variable, where nClasses is the number of classes the response variable may assume.
  - setCostMatrix
```
public void setCostMatrix(double[][] costMatrix)
```
    Specifies the cost matrix for a categorical response variable.
    
    Parameters:
    
    costMatrix - a square double matrix of dimension nClasses by nClasses containing elements C(i, j), the cost of misclassifying a response in class j as in class i. The diagonal elements of the cost matrix must be 0.
    Both dimensions of costMatrix should agree with the number of classes found in the data. Otherwise an exception will be thrown.
    
    Default: costMatrix[i][j]=1.0 where \(i\ne j \) and costMatrix[i][i]=0.0.
  - getMaxNumberOfIterations
```
public int getMaxNumberOfIterations()
```
    Returns the maximum number of iterations allowed for the fitting procedure or training algorithm.
    
    Returns:
    
    an int, the maximum number of iterations
  - setMaxNumberOfIterations
```
public void setMaxNumberOfIterations(int maxIterations)
```
    Sets the maximum number of iterations allowed for the fitting procedure or training algorithm.
    Most predictive models use iterative procedures to fit or train the model. Adjusting the maximum number of iterations up or down can assist in diagnosing problems.
    
    Parameters:
    
    maxIterations - an int specifying the maximum number of iterations
    Default: maxIterations=1000
  - getMaxNumberOfCategories
```
public int getMaxNumberOfCategories()
```
    Returns the maximum number of categories allowed.
    
    Returns:
    
    an int indicating the maximum number of categories allowed within the predictor and response variables.
  - setMaxNumberOfCategories
```
public void setMaxNumberOfCategories(int maxCategories)
```
    Sets the maximum number of categories allowed within categorical predictor and response variables.
    
    Parameters:
    
    maxCategories - an int specifying the maximum number of categories a predictor or response variable can have.
    Default: maxCategories=Math.max(10, maxCat + 1), where maxCat is the maximum category within all categorical predictor and response variables.
  - getNumberOfClasses
```
public int getNumberOfClasses()
```
    Returns the number of unique classes found in the categorical response data.
    
    Returns:
    
    an int indicating the number of unique classes found in the categorical response data
  - setNumberOfClasses
```
public void setNumberOfClasses(int nClasses)
```
    Sets the number of distinct classes of the response variable. Only applies when the response variable is of type PredictiveModel.VariableType.CATEGORICAL or PredictiveModel.VariableType.ORDERED_DISCRETE.
    
    Parameters:
    
    nClasses - an int representing the number of distinct classes or categories of the response variable
    An error is generated if more than nClasses categories are discovered in the data.
    
    Default: nClasses is 0.
  - getNumberOfColumns
```
public int getNumberOfColumns()
```
    Returns the number of columns in the training data xy.
    
    Returns:
    
    an int, the number of columns in xy. If xy is null, nCols=0.
  - getNumberOfMissing
```
public int getNumberOfMissing()
```
    Returns the number of missing values of the response variable found in the data xy.
    
    Returns:
    
    an int, the number of missing values
  - getNumberOfPredictors
```
public int getNumberOfPredictors()
```
    Returns the number of predictors.
    
    Returns:
    
    an int, the number of predictors
  - getNumberOfRows
```
public int getNumberOfRows()
```
    Returns the number of rows in xy (observations).
    
    Returns:
    
    an int, the number of rows in xy (observations)
  - getPredictorIndexes
```
public int[] getPredictorIndexes()
```
    Returns the column indices of xy in which the predictor variables reside.
    
    Returns:
    
    an int array containing the column indices
  - setPredictorIndex
```
public void setPredictorIndex(int[] predIdx)
```
    Sets the column indices of xy in which the predictor variables reside.
    This may be used to subset the full set of predictor variables (PredictiveModel.getPredictorTypes()).
    
    Parameters:
    
    predIdx - an int array containing the column index for each predictor variable Default: All columns other than the column containing the response variable are indicated.
  - getPredictorTypes
```
public PredictiveModel.VariableType[] getPredictorTypes()
```
    Returns an array of VariableType objects that correspond to the predictor data types in xy.
    
    Returns:
    
    a VariableType array that corresponds to the predictor data types in xy
  - setPredictorTypes
```
public void setPredictorTypes(PredictiveModel.VariableType[] predVarType)
```
    Sets the VariableType objects that correspond to the predictor data types in xy.
    
    Parameters:
    
    predVarType - a VariableType array of length equal to the number of predictors specifying the data type of each predictor
  - setRandomObject
```
public void setRandomObject(Random r)
```
    Sets the random object to be used in the permutation of observation data.
    
    Parameters:
    
    r - a Random object to be used in the random permutation of observation data
    Specifying a seed for the Random object can produce repeatable/deterministic output.
  - getRandomObject
```
public Random getRandomObject()
```
    Returns the random object being used in the permutation of the observations.
    
    Returns:
    
    a Random object being used for permutations
  - getNumberOfUniquePredictorValues
```
public int[] getNumberOfUniquePredictorValues()
```
    Returns an array containing the number of distinct values of each predictor found in the input data.
    For continuous predictor variables, the value is set to 0 and is not meaningful.
    
    Returns:
    
    an int array containing the number of distinct values for each predictor
  - getPrintLevel
```
public int getPrintLevel()
```
    Returns the current print level.
    
    Returns:
    
    an int, the current print level
    
    printLevel Action
    
    0 No printing.
    
    1 Prints final results only.
    
    2 Prints intermediate and final results.
    
    Default: printLevel = 0.
  - setPrintLevel
```
public void setPrintLevel(int printLevel)
```
    Sets the print level for a PredictiveModel.
    
    Parameters:
    
    printLevel - an int specifying the level of printing to perform
    
    printLevel Action
    
    0 No printing.
    
    1 Prints final results only.
    
    2 Prints intermediate and final results.
    
    Default: printLevel = 0.
  - getClassProbabilities
```
public double[][] getClassProbabilities()
```
    Returns a matrix containing the predicted class probabilities for each observation in the training data
    
    Returns:
    
    a double matrix containing the class probabilities
  - setClassProbabilities
```
public void setClassProbabilities(double[][] probs)
                           throws PredictiveModel.SumOfProbabilitiesNotOneException
```
    Sets the class probabilities.
    
    Parameters:
    
    probs - a double matrix specifying class probabilities for each pattern or observation in a data set
    The probabilities must range between 0.0 and 1.0 inclusive, and sum to 1.0. The number of columns in probs should agree with the number of classes found in the data. Otherwise an exception is thrown. Calling this method overwrites any existing values.
    
    Default: probs=null unless estimated by an overriding method or set by the user.
    
    Throws:
    
    PredictiveModel.SumOfProbabilitiesNotOneException - is thrown when class probabilities do not sum to 1.0.
  - getPriorProbabilities
```
public double[] getPriorProbabilities()
```
    Returns an array containing the prior probabilities.
    
    Returns:
    
    a double array containing the prior probabilities
  - setPriorProbabilities
```
public void setPriorProbabilities(double[] priors)
                           throws PredictiveModel.SumOfProbabilitiesNotOneException
```
    Sets the prior probabilities for class membership.
    
    Parameters:
    
    priors - a double array specifying the prior probabilities
    The prior probabilities must range between 0.0 and 1.0 inclusive, and sum to 1.0. The length of priors should agree with the number of classes found in the data. Otherwise an exception is thrown. Calling this method overwrites any existing values.
    
    Default: Determined from the data.
    
    Throws:
    
    PredictiveModel.SumOfProbabilitiesNotOneException - is thrown when prior probabilities do not sum to 1.0.
  - getResponseColumnIndex
```
public int getResponseColumnIndex()
```
    Returns the column index in xy containing the response variable.
    
    Returns:
    
    an int, the column index for the response variable
  - setResponseColumnIndex
```
public void setResponseColumnIndex(int index)
```
    Sets the column index in xy containing the response variable.
    
    Parameters:
    
    index - an int, the column index for the response variable
  - getResponseVariableAverage
```
public double getResponseVariableAverage()
```
    Returns the weighted average value of the response variable.
    
    Returns:
    
    a double, the weighted average value of the response variable
  - getResponseVariableMostFrequentClass
```
public int getResponseVariableMostFrequentClass()
```
    Returns the most frequent value of the response variable. Only meaningful for VariableType.CATEGORICAL or VariableType.ORDERED_DISCRETE.
    
    Returns:
    
    an int, the level of the most frequent class
  - getResponseVariableType
```
public PredictiveModel.VariableType getResponseVariableType()
```
    Returns the variable type of the response variable.
    
    Returns:
    
    the VariableType of the response variable
  - getTotalWeight
```
public double getTotalWeight()
```
    Returns the sum of the active case weights.
    
    Returns:
    
    a double, the sum of the active case weights
  - getVariableType
```
public PredictiveModel.VariableType[] getVariableType()
```
    Returns an array containing the variable types in xy.
    
    Returns:
    
    a VariableType array containing the variable types in xy
  - setVariableType
```
public void setVariableType(PredictiveModel.VariableType[] varType)
```
    Sets the variable types for the data.
    
    Parameters:
    
    varType - a PredictiveModel.VariableType array of length equal to xy[0].length containing the type of each variable
  - getWeights
```
public double[] getWeights()
```
    Returns an array containing the case weights.
    
    Returns:
    
    a double array containing the case weights
  - setWeights
```
public void setWeights(double[] weights)
```
    Specifies the case weights.
    
    Parameters:
    
    weights - a double array specifying case weights
    Default: weights[i] = 1.0 for all i.
  - setTrainingData
```
public void setTrainingData(double[][] xy,
                            int responseColumnIndex,
                            PredictiveModel.VariableType[] varType)
```
    Sets up the training data for the predictive model.
    By calling this method, the problem is either initialized or reset to use the data in the arguments.
    
    Parameters:
    
    xy - a double matrix containing the training data and associated response values
    
    responseColumnIndex - an int specifying the column index in xy of the response variable
    
    varType - a PredictiveModel.VariableType array of length equal to xy[0].length containing the type of each variable
  - getXY
```
public double[][] getXY()
```
    Returns a copy of the xy data.
    
    Returns:
    
    a double matrix containing the training data
  - isMustFitModel
```
public boolean isMustFitModel()
```
    Returns the current value of the mustFitModel flag.
    When true, the PredictiveModel.fitModel() method should be called before doing any predictions or other analysis.
    
    Returns:
    
    a boolean indicating the state of the flag
  - isConstantSeries
```
public boolean isConstantSeries()
```
    Returns the current value of the constantSeries flag.
    The flag is set to true if the code determines that the response variable is constant in the training data. The method fitModel will fail if the series is constant. The flag will be reset if the training data is changed using setTrainingData, and the response variable is not constant.
    
    Returns:
    
    a boolean indicating the state of the flag
  - isUserFixedNClasses
```
public boolean isUserFixedNClasses()
```
    Returns true if the number of classes was fixed by the user.
    
    Returns:
    
    a boolean indicating the state of the flag

printLevel	Action
0	No printing.
1	Prints final results only.
2	Prints intermediate and final results.

Class PredictiveModel

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

PredictiveModel

PredictiveModel

Method Detail

clone

setConfiguration

predict

predict

predict

fitModel

getClassErrors

getClassCounts

setClassCounts

setClassLabels

getClassLabels

setMustFitModel

getCostMatrix

setCostMatrix

getMaxNumberOfIterations

setMaxNumberOfIterations

getMaxNumberOfCategories

setMaxNumberOfCategories

getNumberOfClasses

setNumberOfClasses

getNumberOfColumns

getNumberOfMissing

getNumberOfPredictors

getNumberOfRows

getPredictorIndexes

setPredictorIndex

getPredictorTypes

setPredictorTypes

setRandomObject

getRandomObject

getNumberOfUniquePredictorValues

getPrintLevel

setPrintLevel

getClassProbabilities

setClassProbabilities

getPriorProbabilities

setPriorProbabilities

getResponseColumnIndex

setResponseColumnIndex

getResponseVariableAverage

getResponseVariableMostFrequentClass

getResponseVariableType

getTotalWeight

getVariableType

setVariableType

getWeights

setWeights

setTrainingData

getXY

isMustFitModel

isConstantSeries

isUserFixedNClasses