weka.classifiers.evaluation
Class CostCurve

java.lang.Object
  weka.classifiers.evaluation.CostCurve

public class CostCurve

extends java.lang.Object

Generates points illustrating probablity cost tradeoffs that can be obtained by varying the threshold value between classes. For example, the typical threshold value of 0.5 means the predicted probability of "positive" must be higher than 0.5 for the instance to be predicted as "positive".

Version:

$Revision: 1.6 $

Author:

Mark Hall (mhall@cs.waikato.ac.nz)

Field Summary

static java.lang.String	NORM_EXPECTED_COST_NAME
static java.lang.String	PROB_COST_FUNC_NAME
static java.lang.String	RELATION_NAME The name of the relation used in cost curve datasets
static java.lang.String	THRESHOLD_NAME

Constructor Summary

CostCurve()

Method Summary

Instances	getCurve(FastVector predictions) Calculates the performance stats for the default class and return results as a set of Instances.
Instances	getCurve(FastVector predictions, int classIndex) Calculates the performance stats for the desired class and return results as a set of Instances.
static void	main(java.lang.String[] args) Tests the CostCurve generation from the command line.
private Instances	makeHeader()

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

RELATION_NAME

public static final java.lang.String RELATION_NAME

The name of the relation used in cost curve datasets

See Also:

Constant Field Values

PROB_COST_FUNC_NAME

public static final java.lang.String PROB_COST_FUNC_NAME

See Also:

Constant Field Values

NORM_EXPECTED_COST_NAME

public static final java.lang.String NORM_EXPECTED_COST_NAME

See Also:

Constant Field Values

THRESHOLD_NAME

public static final java.lang.String THRESHOLD_NAME

See Also:

Constant Field Values

Constructor Detail

CostCurve

public CostCurve()

Method Detail

getCurve

public Instances getCurve(FastVector predictions)

Calculates the performance stats for the default class and return results as a set of Instances. The structure of these Instances is as follows:

• Probability Cost Function
• Normalized Expected Cost
• Threshold contains the probability threshold that gives rise to the previous performance values.

Returns:

datapoints as a set of instances, null if no predictions have been made.

See Also:

TwoClassStats

getCurve

public Instances getCurve(FastVector predictions,
                          int classIndex)

Calculates the performance stats for the desired class and return results as a set of Instances.

Parameters:

classIndex - index of the class of interest.

Returns:

datapoints as a set of instances.

makeHeader

private Instances makeHeader()

main

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

Tests the CostCurve generation from the command line. The classifier is currently hardcoded. Pipe in an arff file.

Parameters:

args - currently ignored