EvaluateModel

java.lang.Object
- keel.Algorithms.Decision_Trees.M5.EvaluateModel

public class EvaluateModel
extends java.lang.Object

Class for evaluating machine learning models.

Constructor Summary

Constructors
Constructor and Description

EvaluateModel(M5Instances data)
Initializes all the counters for the evaluation and also takes a cost matrix as parameter.

Constructors
Constructor and Description
`EvaluateModel(M5Instances data)` Initializes all the counters for the evaluation and also takes a cost matrix as parameter.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`avgCost()` Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
`double[][]`	`confusionMatrix()` Returns a copy of the confusion matrix.
`double`	`correct()` Gets the number of instances correctly classified (that is, for which a correct prediction was made).
`double`	`correlationCoefficient()` Returns the correlation coefficient if the class is numeric.
`void`	`crossValidateModel(M5 classifier, M5Instances data, int numFolds)` Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
`void`	`crossValidateModel(java.lang.String classifierString, M5Instances data, int numFolds, java.lang.String[] options)` Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
`boolean`	`equals(java.lang.Object obj)` Tests whether the current evaluation object is equal to another evaluation object
`double`	`errorRate()` Returns the estimated error rate or the root mean squared error (if the class is numeric).
`void`	`evaluateModel(M5 classifier, M5Instances data)` Evaluates the classifier on a given set of instances.
`static java.lang.String`	`evaluateModel(M5 classifier, java.lang.String[] options)` Evaluates a classifier with the options given in an array of strings.
`static java.lang.String`	`evaluateModel(java.lang.String classifierString, java.lang.String[] options)` Evaluate the model M5 by executing the function evaluateModel(M5 classifier, String[] options).
`double`	`evaluateModelOnce(double[] dist, M5Instance instance)` Evaluates the supplied distribution on a single instance.
`void`	`evaluateModelOnce(double prediction, M5Instance instance)` Evaluates the supplied prediction on a single instance.
`double`	`evaluateModelOnce(M5 classifier, M5Instance instance)` Evaluates the classifier on a single instance.
`double`	`falseNegativeRate(int classIndex)` Calculate the false negative rate with respect to a particular class.
`double`	`falsePositiveRate(int classIndex)` Calculate the false positive rate with respect to a particular class.
`double`	`fMeasure(int classIndex)` Calculate the F-Measure with respect to a particular class.
`double`	`incorrect()` Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made).
`double`	`kappa()` Returns value of kappa statistic if class is nominal.
`double`	`KBInformation()` Return the total Kononenko & Bratko Information score in bits
`double`	`KBMeanInformation()` Return the Kononenko & Bratko Information score in bits per instance.
`double`	`KBRelativeInformation()` Return the Kononenko & Bratko Relative Information score
`static void`	`main(java.lang.String[] args)` A test method for this class.
`double`	`meanAbsoluteError()` Returns the mean absolute error.
`double`	`meanPriorAbsoluteError()` Returns the mean absolute error of the prior.
`double`	`numFalseNegatives(int classIndex)` Calculate number of false negatives with respect to a particular class.
`double`	`numFalsePositives(int classIndex)` Calculate number of false positives with respect to a particular class.
`double`	`numInstances()` Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
`double`	`numTrueNegatives(int classIndex)` Calculate the number of true negatives with respect to a particular class.
`double`	`numTruePositives(int classIndex)` Calculate the number of true positives with respect to a particular class.
`double`	`pctCorrect()` Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
`double`	`pctIncorrect()` Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
`double`	`pctUnclassified()` Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
`double`	`precision(int classIndex)` Calculate the precision with respect to a particular class.
`double`	`priorEntropy()` Calculate the entropy of the prior distribution
`double`	`recall(int classIndex)` Calculate the recall with respect to a particular class.
`double`	`relativeAbsoluteError()` Returns the relative absolute error.
`double`	`rootMeanPriorSquaredError()` Returns the root mean prior squared error.
`double`	`rootMeanSquaredError()` Returns the root mean squared error.
`double`	`rootRelativeSquaredError()` Returns the root relative squared error if the class is numeric.
`static double`	`roundNum(double num)` To round to 4 decimal in a double.
`void`	`setPriors(M5Instances train)` Sets the class prior probabilities
`double`	`SFEntropyGain()` Returns the total SF, which is the null model entropy minus the scheme entropy.
`double`	`SFMeanEntropyGain()` Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
`double`	`SFMeanPriorEntropy()` Returns the entropy per instance for the null model
`double`	`SFMeanSchemeEntropy()` Returns the entropy per instance for the scheme
`double`	`SFPriorEntropy()` Returns the total entropy for the null model
`double`	`SFSchemeEntropy()` Returns the total entropy for the scheme
`java.lang.String`	`toClassDetailsString()` Returns a string with the deatails of this class.
`java.lang.String`	`toClassDetailsString(java.lang.String title)` Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure.
`java.lang.String`	`toCumulativeMarginDistributionString()` Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
`java.lang.String`	`toMatrixString()` Calls toMatrixString() with a default title.
`java.lang.String`	`toMatrixString(java.lang.String title)` Outputs the performance statistics as a classification confusion matrix.
`java.lang.String`	`toSummaryString()` Calls toSummaryString() with no title and no complexity stats
`java.lang.String`	`toSummaryString(boolean printComplexityStatistics)` Calls toSummaryString() with a default title.
`java.lang.String`	`toSummaryString(java.lang.String title, boolean printComplexityStatistics)` Outputs the performance statistics in summary form.
`double`	`totalCost()` Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
`double`	`trueNegativeRate(int classIndex)` Calculate the true negative rate with respect to a particular class.
`double`	`truePositiveRate(int classIndex)` Calculate the true positive rate with respect to a particular class.
`double`	`unclassified()` Gets the number of instances not classified (that is, for which no prediction was made by the classifier).
`void`	`updatePriors(M5Instance instance)` Updates the class prior probabilities (when incrementally training)

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

- Constructor Detail
  - EvaluateModel
```
public EvaluateModel(M5Instances data)
              throws java.lang.Exception
```
    Initializes all the counters for the evaluation and also takes a cost matrix as parameter.
    
    Parameters:
    
    data - set of instances, to get some header information
    
    Throws:
    
    java.lang.Exception - if cost matrix is not compatible with data, the class is not defined or the class is numeric
- Method Detail
  - confusionMatrix
```
public double[][] confusionMatrix()
```
    Returns a copy of the confusion matrix.
    
    Returns:
    
    a copy of the confusion matrix as a two-dimensional array
  - crossValidateModel
```
public void crossValidateModel(M5 classifier,
                               M5Instances data,
                               int numFolds)
                        throws java.lang.Exception
```
    Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
    
    Parameters:
    
    classifier - the classifier with any options set.
    
    data - the data on which the cross-validation is to be performed
    
    numFolds - the number of folds for the cross-validation
    
    Throws:
    
    java.lang.Exception - if a classifier could not be generated successfully or the class is not defined
  - crossValidateModel
```
public void crossValidateModel(java.lang.String classifierString,
                               M5Instances data,
                               int numFolds,
                               java.lang.String[] options)
                        throws java.lang.Exception
```
    Performs a (stratified if class is nominal) cross-validation for a classifier on a set of instances.
    
    Parameters:
    
    classifierString - a string naming the class of the classifier
    
    data - the data on which the cross-validation is to be performed
    
    numFolds - the number of folds for the cross-validation
    
    options - the options to the classifier. Any options accepted by the classifier will be removed from this array.
    
    Throws:
    
    java.lang.Exception - if a classifier could not be generated successfully or the class is not defined
  - evaluateModel
```
public static java.lang.String evaluateModel(java.lang.String classifierString,
                                             java.lang.String[] options)
                                      throws java.lang.Exception
```
    Evaluate the model M5 by executing the function evaluateModel(M5 classifier, String[] options). Check documentation for more information in the documentation of the function: evaluateModel(M5 classifier, String[] options)
    
    Parameters:
    
    classifierString - classifier name.
    
    options - the array of string containing the options
    
    Returns:
    
    a string describing the results
    
    Throws:
    
    java.lang.Exception - if model could not be evaluated successfully
  - main
```
public static void main(java.lang.String[] args)
```
    A test method for this class. Just extracts the first command line argument as a classifier class name and calls evaluateModel.
    
    Parameters:
    
    args - an array of command line arguments, the first of which must be the class name of a classifier.
  - evaluateModel
```
public static java.lang.String evaluateModel(M5 classifier,
                                             java.lang.String[] options)
                                      throws java.lang.Exception
```
    Evaluates a classifier with the options given in an array of strings.
    Valid options are:
    -t name of training file
    Name of the file with the training data. (required)
    -T name of test file
    Name of the file with the test data. If missing a cross-validation is performed.
    -c class index
    Index of the class attribute (1, 2, ...; default: last).
    -x number of folds
    The number of folds for the cross-validation (default: 10).
    -s random number seed
    Random number seed for the cross-validation (default: 1).
    -m file with cost matrix
    The name of a file containing a cost matrix.
    -l name of model input file
    Loads classifier from the given file.
    -d name of model output file
    Saves classifier built from the training data into the given file.
    -v
    Outputs no statistics for the training data.
    -o
    Outputs statistics only, not the classifier.
    -i
    Outputs detailed information-retrieval statistics per class.
    -k
    Outputs information-theoretic statistics.
    -p
    Outputs predictions for test instances (and nothing else).
    -r
    Outputs cumulative margin distribution (and nothing else).
    -g
    Only for classifiers that implement "Graphable." Outputs the graph representation of the classifier (and nothing else).
    
    Parameters:
    
    classifier - machine learning classifier
    
    options - the array of string containing the options
    
    Returns:
    
    a string describing the results
    
    Throws:
    
    java.lang.Exception - if model could not be evaluated successfully
  - evaluateModel
```
public void evaluateModel(M5 classifier,
                          M5Instances data)
                   throws java.lang.Exception
```
    Evaluates the classifier on a given set of instances.
    
    Parameters:
    
    classifier - machine learning classifier
    
    data - set of test instances for evaluation
    
    Throws:
    
    java.lang.Exception - if model could not be evaluated successfully
  - evaluateModelOnce
```
public double evaluateModelOnce(M5 classifier,
                                M5Instance instance)
                         throws java.lang.Exception
```
    Evaluates the classifier on a single instance.
    
    Parameters:
    
    classifier - machine learning classifier
    
    instance - the test instance to be classified
    
    Returns:
    
    the prediction made by the clasifier
    
    Throws:
    
    java.lang.Exception - if model could not be evaluated successfully or the data contains string attributes
  - evaluateModelOnce
```
public double evaluateModelOnce(double[] dist,
                                M5Instance instance)
                         throws java.lang.Exception
```
    Evaluates the supplied distribution on a single instance.
    
    Parameters:
    
    dist - the supplied distribution
    
    instance - the test instance to be classified
    
    Returns:
    
    predition done.
    
    Throws:
    
    java.lang.Exception - if model could not be evaluated successfully
  - evaluateModelOnce
```
public void evaluateModelOnce(double prediction,
                              M5Instance instance)
                       throws java.lang.Exception
```
    Evaluates the supplied prediction on a single instance.
    
    Parameters:
    
    prediction - the supplied prediction
    
    instance - the test instance to be classified
    
    Throws:
    
    java.lang.Exception - if model could not be evaluated successfully
  - numInstances
```
public final double numInstances()
```
    Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value).
    
    Returns:
    
    the number of test instances with known class
  - incorrect
```
public final double incorrect()
```
    Gets the number of instances incorrectly classified (that is, for which an incorrect prediction was made). (Actually the sum of the weights of these instances)
    
    Returns:
    
    the number of incorrectly classified instances
  - pctIncorrect
```
public final double pctIncorrect()
```
    Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).
    
    Returns:
    
    the percent of incorrectly classified instances (between 0 and 100)
  - totalCost
```
public final double totalCost()
```
    Gets the total cost, that is, the cost of each prediction times the weight of the instance, summed over all instances.
    
    Returns:
    
    the total cost
  - avgCost
```
public final double avgCost()
```
    Gets the average cost, that is, total cost of misclassifications (incorrect plus unclassified) over the total number of instances.
    
    Returns:
    
    the average cost.
  - correct
```
public final double correct()
```
    Gets the number of instances correctly classified (that is, for which a correct prediction was made). (Actually the sum of the weights of these instances)
    
    Returns:
    
    the number of correctly classified instances
  - pctCorrect
```
public final double pctCorrect()
```
    Gets the percentage of instances correctly classified (that is, for which a correct prediction was made).
    
    Returns:
    
    the percent of correctly classified instances (between 0 and 100)
  - unclassified
```
public final double unclassified()
```
    Gets the number of instances not classified (that is, for which no prediction was made by the classifier). (Actually the sum of the weights of these instances)
    
    Returns:
    
    the number of unclassified instances
  - pctUnclassified
```
public final double pctUnclassified()
```
    Gets the percentage of instances not classified (that is, for which no prediction was made by the classifier).
    
    Returns:
    
    the percent of unclassified instances (between 0 and 100)
  - errorRate
```
public final double errorRate()
```
    Returns the estimated error rate or the root mean squared error (if the class is numeric). If a cost matrix was given this error rate gives the average cost.
    
    Returns:
    
    the estimated error rate (between 0 and 1, or between 0 and maximum cost)
  - kappa
```
public final double kappa()
```
    Returns value of kappa statistic if class is nominal.
    
    Returns:
    
    the value of the kappa statistic
  - correlationCoefficient
```
public final double correlationCoefficient()
                                    throws java.lang.Exception
```
    Returns the correlation coefficient if the class is numeric.
    
    Returns:
    
    the correlation coefficient
    
    Throws:
    
    java.lang.Exception - if class is not numeric
  - meanAbsoluteError
```
public final double meanAbsoluteError()
```
    Returns the mean absolute error. Refers to the error of the predicted values for numeric classes, and the error of the predicted probability distribution for nominal classes.
    
    Returns:
    
    the mean absolute error
  - meanPriorAbsoluteError
```
public final double meanPriorAbsoluteError()
```
    Returns the mean absolute error of the prior.
    
    Returns:
    
    the mean absolute error
  - relativeAbsoluteError
```
public final double relativeAbsoluteError()
                                   throws java.lang.Exception
```
    Returns the relative absolute error.
    
    Returns:
    
    the relative absolute error
    
    Throws:
    
    java.lang.Exception - if it can't be computed
  - rootMeanSquaredError
```
public final double rootMeanSquaredError()
```
    Returns the root mean squared error.
    
    Returns:
    
    the root mean squared error
  - rootMeanPriorSquaredError
```
public final double rootMeanPriorSquaredError()
```
    Returns the root mean prior squared error.
    
    Returns:
    
    the root mean prior squared error
  - rootRelativeSquaredError
```
public final double rootRelativeSquaredError()
```
    Returns the root relative squared error if the class is numeric.
    
    Returns:
    
    the root relative squared error
  - priorEntropy
```
public final double priorEntropy()
                          throws java.lang.Exception
```
    Calculate the entropy of the prior distribution
    
    Returns:
    
    the entropy of the prior distribution
    
    Throws:
    
    java.lang.Exception - if the class is not nominal
  - KBInformation
```
public final double KBInformation()
                           throws java.lang.Exception
```
    Return the total Kononenko & Bratko Information score in bits
    
    Returns:
    
    the K&B information score
    
    Throws:
    
    java.lang.Exception - if the class is not nominal
  - KBMeanInformation
```
public final double KBMeanInformation()
                               throws java.lang.Exception
```
    Return the Kononenko & Bratko Information score in bits per instance.
    
    Returns:
    
    the K&B information score
    
    Throws:
    
    java.lang.Exception - if the class is not nominal
  - KBRelativeInformation
```
public final double KBRelativeInformation()
                                   throws java.lang.Exception
```
    Return the Kononenko & Bratko Relative Information score
    
    Returns:
    
    the K&B relative information score
    
    Throws:
    
    java.lang.Exception - if the class is not nominal
  - SFPriorEntropy
```
public final double SFPriorEntropy()
```
    Returns the total entropy for the null model
    
    Returns:
    
    the total null model entropy
  - SFMeanPriorEntropy
```
public final double SFMeanPriorEntropy()
```
    Returns the entropy per instance for the null model
    
    Returns:
    
    the null model entropy per instance
  - SFSchemeEntropy
```
public final double SFSchemeEntropy()
```
    Returns the total entropy for the scheme
    
    Returns:
    
    the total scheme entropy
  - SFMeanSchemeEntropy
```
public final double SFMeanSchemeEntropy()
```
    Returns the entropy per instance for the scheme
    
    Returns:
    
    the scheme entropy per instance
  - SFEntropyGain
```
public final double SFEntropyGain()
```
    Returns the total SF, which is the null model entropy minus the scheme entropy.
    
    Returns:
    
    the total SF
  - SFMeanEntropyGain
```
public final double SFMeanEntropyGain()
```
    Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
    
    Returns:
    
    the SF per instance
  - toCumulativeMarginDistributionString
```
public java.lang.String toCumulativeMarginDistributionString()
                                                      throws java.lang.Exception
```
    Output the cumulative margin distribution as a string suitable for input for gnuplot or similar package.
    
    Returns:
    
    the cumulative margin distribution
    
    Throws:
    
    java.lang.Exception - if the class attribute is nominal
  - toSummaryString
```
public java.lang.String toSummaryString()
```
    Calls toSummaryString() with no title and no complexity stats
    
    Returns:
    
    a summary description of the classifier evaluation
  - toSummaryString
```
public java.lang.String toSummaryString(boolean printComplexityStatistics)
```
    Calls toSummaryString() with a default title.
    
    Parameters:
    
    printComplexityStatistics - if true, complexity statistics are returned as well
    
    Returns:
    
    String summarizing details of the model and its statistics.
  - toSummaryString
```
public java.lang.String toSummaryString(java.lang.String title,
                                        boolean printComplexityStatistics)
```
    Outputs the performance statistics in summary form. Lists number (and percentage) of instances classified correctly, incorrectly and unclassified. Outputs the total number of instances classified, and the number of instances (if any) that had no class value provided.
    
    Parameters:
    
    title - the title for the statistics
    
    printComplexityStatistics - if true, complexity statistics are returned as well
    
    Returns:
    
    the summary as a String
  - toMatrixString
```
public java.lang.String toMatrixString()
                                throws java.lang.Exception
```
    Calls toMatrixString() with a default title.
    
    Returns:
    
    the confusion matrix as a string
    
    Throws:
    
    java.lang.Exception - if the class is numeric
  - toMatrixString
```
public java.lang.String toMatrixString(java.lang.String title)
                                throws java.lang.Exception
```
    Outputs the performance statistics as a classification confusion matrix. For each class value, shows the distribution of predicted class values.
    
    Parameters:
    
    title - the title for the confusion matrix
    
    Returns:
    
    the confusion matrix as a String
    
    Throws:
    
    java.lang.Exception - if the class is numeric
  - toClassDetailsString
```
public java.lang.String toClassDetailsString()
                                      throws java.lang.Exception
```
    Returns a string with the deatails of this class.
    
    Returns:
    
    string with the deatails of this class.
    
    Throws:
    
    java.lang.Exception
  - toClassDetailsString
```
public java.lang.String toClassDetailsString(java.lang.String title)
                                      throws java.lang.Exception
```
    Generates a breakdown of the accuracy for each class, incorporating various information-retrieval statistics, such as true/false positive rate, precision/recall/F-Measure. Should be useful for ROC curves, recall/precision curves.
    
    Parameters:
    
    title - the title to prepend the stats string with
    
    Returns:
    
    the statistics presented as a string
    
    Throws:
    
    java.lang.Exception - if the class is not nominal.
  - numTruePositives
```
public double numTruePositives(int classIndex)
```
    Calculate the number of true positives with respect to a particular class. This is defined as
```
 correctly classified positives
 
```
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the true positive rate
  - truePositiveRate
```
public double truePositiveRate(int classIndex)
```
    Calculate the true positive rate with respect to a particular class. This is defined as
```
 correctly classified positives
 ------------------------------
       total positives
 
```
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the true positive rate
  - numTrueNegatives
```
public double numTrueNegatives(int classIndex)
```
    Calculate the number of true negatives with respect to a particular class. This is defined as
```
 correctly classified negatives
 
```
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the true positive rate
  - trueNegativeRate
```
public double trueNegativeRate(int classIndex)
```
    Calculate the true negative rate with respect to a particular class. This is defined as
```
 correctly classified negatives
 ------------------------------
       total negatives
 
```
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the true positive rate
  - numFalsePositives
```
public double numFalsePositives(int classIndex)
```
    Calculate number of false positives with respect to a particular class. This is defined as
```
 incorrectly classified negatives
 
```
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the false positive rate
  - falsePositiveRate
```
public double falsePositiveRate(int classIndex)
```
    Calculate the false positive rate with respect to a particular class. This is defined as
```
 incorrectly classified negatives
 --------------------------------
        total negatives
 
```
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the false positive rate
  - numFalseNegatives
```
public double numFalseNegatives(int classIndex)
```
    Calculate number of false negatives with respect to a particular class. This is defined as
```
 incorrectly classified positives
 
```
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the false positive rate
  - falseNegativeRate
```
public double falseNegativeRate(int classIndex)
```
    Calculate the false negative rate with respect to a particular class. This is defined as
```
 incorrectly classified positives
 --------------------------------
        total positives
 
```
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the false positive rate
  - recall
```
public double recall(int classIndex)
```
    Calculate the recall with respect to a particular class. This is defined as
```
 correctly classified positives
 ------------------------------
       total positives
 
```
    (Which is also the same as the truePositiveRate.)
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the recall
  - precision
```
public double precision(int classIndex)
```
    Calculate the precision with respect to a particular class. This is defined as
```
 correctly classified positives
 ------------------------------
  total predicted as positive
 
```
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the precision
  - fMeasure
```
public double fMeasure(int classIndex)
```
    Calculate the F-Measure with respect to a particular class. This is defined as
```
 2 * recall * precision
 ----------------------
   recall + precision
 
```
    Parameters:
    
    classIndex - the index of the class to consider as "positive"
    
    Returns:
    
    the F-Measure
  - setPriors
```
public void setPriors(M5Instances train)
               throws java.lang.Exception
```
    Sets the class prior probabilities
    
    Parameters:
    
    train - the training instances used to determine the prior probabilities
    
    Throws:
    
    java.lang.Exception - if the class attribute of the instances is not set
  - updatePriors
```
public void updatePriors(M5Instance instance)
                  throws java.lang.Exception
```
    Updates the class prior probabilities (when incrementally training)
    
    Parameters:
    
    instance - the new training instance seen
    
    Throws:
    
    java.lang.Exception - if the class of the instance is not set
  - equals
```
public boolean equals(java.lang.Object obj)
```
    Tests whether the current evaluation object is equal to another evaluation object
    
    Overrides:
    
    equals in class java.lang.Object
    
    Parameters:
    
    obj - the object to compare against
    
    Returns:
    
    true if the two objects are equal
  - roundNum
```
public static double roundNum(double num)
```
    To round to 4 decimal in a double.
    
    Parameters:
    
    num - number to round
    
    Returns:
    
    rounded number.

Class EvaluateModel

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

EvaluateModel

Method Detail

confusionMatrix

crossValidateModel

crossValidateModel

evaluateModel

main

evaluateModel

evaluateModel

evaluateModelOnce

evaluateModelOnce

evaluateModelOnce

numInstances

incorrect

pctIncorrect

totalCost

avgCost

correct

pctCorrect

unclassified

pctUnclassified

errorRate

kappa

correlationCoefficient

meanAbsoluteError

meanPriorAbsoluteError

relativeAbsoluteError

rootMeanSquaredError

rootMeanPriorSquaredError

rootRelativeSquaredError

priorEntropy

KBInformation

KBMeanInformation

KBRelativeInformation

SFPriorEntropy

SFMeanPriorEntropy

SFSchemeEntropy

SFMeanSchemeEntropy

SFEntropyGain

SFMeanEntropyGain

toCumulativeMarginDistributionString

toSummaryString

toSummaryString

toSummaryString

toMatrixString

toMatrixString

toClassDetailsString

toClassDetailsString

numTruePositives

truePositiveRate

numTrueNegatives

trueNegativeRate

numFalsePositives

falsePositiveRate

numFalseNegatives

falseNegativeRate

recall

precision

fMeasure

setPriors

updatePriors

equals

roundNum