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## weka.classifiers.meta
Class ThresholdSelector

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.SingleClassifierEnhancer
              weka.classifiers.RandomizableSingleClassifierEnhancer
                  weka.classifiers.meta.ThresholdSelector

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, Drawable, OptionHandler, Randomizable

    public class ThresholdSelector

extends RandomizableSingleClassifierEnhancer
implements OptionHandler, Drawable

Class for selecting a threshold on a probability output by a distribution classifier. The threshold is set so that a given performance measure is optimized. Currently this is the F-measure. Performance is measured either on the training data, a hold-out set or using cross-validation. In addition, the probabilities returned by the base learner can have their range expanded so that the output probabilities will reside between 0 and 1 (this is useful if the scheme normally produces probabilities in a very narrow range).

Valid options are:

-C num
The class for which threshold is determined. Valid values are: 1, 2 (for first and second classes, respectively), 3 (for whichever class is least frequent), 4 (for whichever class value is most frequent), and 5 (for the first class named any of "yes","pos(itive)", "1", or method 3 if no matches). (default 5).

-W classname
Specify the full class name of the base classifier.

-X num
Number of folds used for cross validation. If just a hold-out set is used, this determines the size of the hold-out set (default 3).

-R integer
Sets whether confidence range correction is applied. This can be used to ensure the confidences range from 0 to 1. Use 0 for no range correction, 1 for correction based on the min/max values seen during threshold selection (default 0).

-S seed
Random number seed (default 1).

-E integer
Sets the evaluation mode. Use 0 for evaluation using cross-validation, 1 for evaluation using hold-out set, and 2 for evaluation on the training data (default 1).

Options after -- are passed to the designated sub-classifier.

Version:
  $Revision: 1.30.2.3 $
Author:
  Eibe Frank (eibe@cs.waikato.ac.nz)
See Also:
  Serialized Form

Field Summary
`static int` | `EVAL_CROSS_VALIDATION`
`static int` | `EVAL_TRAINING_SET`
`static int` | `EVAL_TUNED_SPLIT`
`static int` | `OPTIMIZE_0`
`static int` | `OPTIMIZE_1`
`static int` | `OPTIMIZE_LFREQ`
`static int` | `OPTIMIZE_MFREQ`
`static int` | `OPTIMIZE_POS_NAME`
`static int` | `RANGE_BOUNDS`
`static int` | `RANGE_NONE`
`static Tag[]` | `TAGS_EVAL`
`static Tag[]` | `TAGS_OPTIMIZE`
`static Tag[]` | `TAGS_RANGE`

Fields inherited from interface weka.core.Drawable
`BayesNet, NOT_DRAWABLE, TREE`

Constructor Summary
`ThresholdSelector()` Constructor.

Method Summary
`void` | `buildClassifier(Instances instances)` Generates the classifier.
`java.lang.String` | `designatedClassTipText()`
`double[]` | `distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
`java.lang.String` | `evaluationModeTipText()`
`SelectedTag` | `getDesignatedClass()` Gets the method to determine which class value to optimize.
`SelectedTag` | `getEvaluationMode()` Gets the evaluation mode used.
`int` | `getNumXValFolds()` Get the number of folds used for cross-validation.
`java.lang.String[]` | `getOptions()` Gets the current settings of the Classifier.
`SelectedTag` | `getRangeCorrection()` Gets the confidence range correction mode used.
`java.lang.String` | `globalInfo()`
`java.lang.String` | `graph()` Returns graph describing the classifier (if possible).
`int` | `graphType()` Returns the type of graph this classifier represents.
`java.util.Enumeration` | `listOptions()` Returns an enumeration describing the available options.
`static void` | `main(java.lang.String[] argv)` Main method for testing this class.
`java.lang.String` | `numXValFoldsTipText()`
`java.lang.String` | `rangeCorrectionTipText()`
`void` | `setDesignatedClass(SelectedTag newMethod)` Sets the method to determine which class value to optimize.
`void` | `setEvaluationMode(SelectedTag newMethod)` Sets the evaluation mode used.
`void` | `setNumXValFolds(int newNumFolds)` Set the number of folds used for cross-validation.
`void` | `setOptions(java.lang.String[] options)` Parses a given list of options.
`void` | `setRangeCorrection(SelectedTag newMethod)` Sets the confidence range correction mode used.
`java.lang.String` | `toString()` Returns description of the cross-validated classifier.

Methods inherited from class weka.classifiers.RandomizableSingleClassifierEnhancer
`getSeed, seedTipText, setSeed`

Methods inherited from class weka.classifiers.SingleClassifierEnhancer
`classifierTipText, getClassifier, setClassifier`

Methods inherited from class weka.classifiers.Classifier
`classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug`

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

Field Detail

### RANGE_NONE

    public static final int RANGE_NONE

See Also:
  Constant Field Values

### RANGE_BOUNDS

    public static final int RANGE_BOUNDS

See Also:
  Constant Field Values

### TAGS_RANGE

    public static final Tag[] TAGS_RANGE

### EVAL_TRAINING_SET

    public static final int EVAL_TRAINING_SET

See Also:
  Constant Field Values

### EVAL_TUNED_SPLIT

    public static final int EVAL_TUNED_SPLIT

See Also:
  Constant Field Values

### EVAL_CROSS_VALIDATION

    public static final int EVAL_CROSS_VALIDATION

See Also:
  Constant Field Values

### TAGS_EVAL

    public static final Tag[] TAGS_EVAL

### OPTIMIZE_0

    public static final int OPTIMIZE_0

See Also:
  Constant Field Values

### OPTIMIZE_1

    public static final int OPTIMIZE_1

See Also:
  Constant Field Values

### OPTIMIZE_LFREQ

    public static final int OPTIMIZE_LFREQ

See Also:
  Constant Field Values

### OPTIMIZE_MFREQ

    public static final int OPTIMIZE_MFREQ

See Also:
  Constant Field Values

### OPTIMIZE_POS_NAME

    public static final int OPTIMIZE_POS_NAME

See Also:
  Constant Field Values

### TAGS_OPTIMIZE

    public static final Tag[] TAGS_OPTIMIZE

Constructor Detail

### ThresholdSelector

    public ThresholdSelector()

  Constructor.

Method Detail

### listOptions

    public java.util.Enumeration listOptions()

  Returns an enumeration describing the available options.

  Specified by:
    `listOptions` in interface `OptionHandler`
  Overrides:
    `listOptions` in class `RandomizableSingleClassifierEnhancer`
  Returns:
    an enumeration of all the available options.

### setOptions

    public void setOptions(java.lang.String[] options)
                    throws java.lang.Exception

  Parses a given list of options. Valid options are:

  -C num
  The class for which threshold is determined. Valid values are: 1, 2 (for first and second classes, respectively), 3 (for whichever class is least frequent), 4 (for whichever class value is most frequent), and 5 (for the first class named any of "yes","pos(itive)", "1", or method 3 if no matches). (default 3).

  -W classname
  Specify the full class name of classifier to perform cross-validation selection on.

  -X num
  Number of folds used for cross validation. If just a hold-out set is used, this determines the size of the hold-out set (default 3).

  -R integer
  Sets whether confidence range correction is applied. This can be used to ensure the confidences range from 0 to 1. Use 0 for no range correction, 1 for correction based on the min/max values seen during threshold selection (default 0).

  -S seed
  Random number seed (default 1).

  -E integer
  Sets the evaluation mode. Use 0 for evaluation using cross-validation, 1 for evaluation using hold-out set, and 2 for evaluation on the training data (default 1).

  Options after -- are passed to the designated sub-classifier.

  Specified by:
    `setOptions` in interface `OptionHandler`
  Overrides:
    `setOptions` in class `RandomizableSingleClassifierEnhancer`
  Parameters:
    `options` - the list of options as an array of strings
  Throws:
    `java.lang.Exception` - if an option is not supported

### getOptions

    public java.lang.String[] getOptions()

  Gets the current settings of the Classifier.

  Specified by:
    `getOptions` in interface `OptionHandler`
  Overrides:
    `getOptions` in class `RandomizableSingleClassifierEnhancer`
  Returns:
    an array of strings suitable for passing to setOptions

### buildClassifier

    public void buildClassifier(Instances instances)
                         throws java.lang.Exception

  Generates the classifier.

  Specified by:
    `buildClassifier` in class `Classifier`
  Parameters:
    `instances` - set of instances serving as training data
  Throws:
    `java.lang.Exception` - if the classifier has not been generated successfully

### distributionForInstance

    public double[] distributionForInstance(Instance instance)
                                     throws java.lang.Exception

  Calculates the class membership probabilities for the given test instance.

  Overrides:
    `distributionForInstance` in class `Classifier`
  Parameters:
    `instance` - the instance to be classified
  Returns:
    predicted class probability distribution
  Throws:
    `java.lang.Exception` - if instance could not be classified successfully

### globalInfo

    public java.lang.String globalInfo()

  Returns:
    a description of the classifier suitable for displaying in the explorer/experimenter gui

### designatedClassTipText

    public java.lang.String designatedClassTipText()

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getDesignatedClass

    public SelectedTag getDesignatedClass()

  Gets the method to determine which class value to optimize. Will be one of OPTIMIZE_0, OPTIMIZE_1, OPTIMIZE_LFREQ, OPTIMIZE_MFREQ, OPTIMIZE_POS_NAME.

  Returns:
    the class selection mode.

### setDesignatedClass

    public void setDesignatedClass(SelectedTag newMethod)

  Sets the method to determine which class value to optimize. Will be one of OPTIMIZE_0, OPTIMIZE_1, OPTIMIZE_LFREQ, OPTIMIZE_MFREQ, OPTIMIZE_POS_NAME.

  Parameters:
    `newMethod` - the new class selection mode.

### evaluationModeTipText

    public java.lang.String evaluationModeTipText()

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setEvaluationMode

    public void setEvaluationMode(SelectedTag newMethod)

  Sets the evaluation mode used. Will be one of EVAL_TRAINING, EVAL_TUNED_SPLIT, or EVAL_CROSS_VALIDATION

  Parameters:
    `newMethod` - the new evaluation mode.

### getEvaluationMode

    public SelectedTag getEvaluationMode()

  Gets the evaluation mode used. Will be one of EVAL_TRAINING, EVAL_TUNED_SPLIT, or EVAL_CROSS_VALIDATION

  Returns:
    the evaluation mode.

### rangeCorrectionTipText

    public java.lang.String rangeCorrectionTipText()

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### setRangeCorrection

    public void setRangeCorrection(SelectedTag newMethod)

  Sets the confidence range correction mode used. Will be one of RANGE_NONE, or RANGE_BOUNDS

  Parameters:
    `newMethod` - the new correciton mode.

### getRangeCorrection

    public SelectedTag getRangeCorrection()

  Gets the confidence range correction mode used. Will be one of RANGE_NONE, or RANGE_BOUNDS

  Returns:
    the confidence correction mode.

### numXValFoldsTipText

    public java.lang.String numXValFoldsTipText()

  Returns:
    tip text for this property suitable for displaying in the explorer/experimenter gui

### getNumXValFolds

    public int getNumXValFolds()

  Get the number of folds used for cross-validation.

  Returns:
    the number of folds used for cross-validation.

### setNumXValFolds

    public void setNumXValFolds(int newNumFolds)

  Set the number of folds used for cross-validation.

  Parameters:
    `newNumFolds` - the number of folds used for cross-validation.

### graphType

    public int graphType()

  Returns the type of graph this classifier represents.

  Specified by:
    `graphType` in interface `Drawable`
  Returns:
    the type of graph representing the object

### graph

    public java.lang.String graph()
                           throws java.lang.Exception

  Returns graph describing the classifier (if possible).

  Specified by:
    `graph` in interface `Drawable`
  Returns:
    the graph of the classifier in dotty format
  Throws:
    `java.lang.Exception` - if the classifier cannot be graphed

### toString

    public java.lang.String toString()

  Returns description of the cross-validated classifier.

  Overrides:
    `toString` in class `java.lang.Object`
  Returns:
    description of the cross-validated classifier as a string

### main

    public static void main(java.lang.String[] argv)

  Main method for testing this class.

  Parameters:
    `argv` - the options

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