com.bayesserver.analysis

Class LiftChart

java.lang.Object

com.bayesserver.analysis.LiftChart

public final class LiftChart
extends Object

Represents a lift chart, used to measure predictive performance. Lift charts are used to measure the predictive performance of a classification network, and tell us how the model compares to a random model and the ideal model.

Lift charts are particularly useful when the probabilities of correct classification are low, such as when predicting rare events or anomalies. In these circumstances, a confusion matrix may give very high accuracy simply because most cases are not rare events or anomalies and hence correctly predicted. A lift chart however can take small probabilities into account to assess the improvement (lift) in prediction of these rare events or anomalies.

Method Summary

Modifier and Type	Method and Description
static LiftChart	create(String actual, String predictedProbability, String caseWeight, Comparable targetValue, DataReaderCommand readerCommand) Creates a lift chart, used to measure predictive performance.
String	getActual() Gets the name of the data column containing the actual classification.
double	getIdeal() Gets the population probability value at which the target reaches 100 %.
List<LiftChartPoint>	getPoints() Gets the xy points that make up the lift chart.
String	getPredictedProbability() Gets the name of the data column which contains the predicted probability generated by an inference query.
double	getScore() Gets the overall score, which is a positive or negative probability between 0 and 1 indicating the classification performance of the network.
Comparable	getTargetValue() Gets the target value which we are interested in.

Methods inherited from class java.lang.Object

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

Method Detail

create

public static LiftChart create(String actual,
                               String predictedProbability,
                               String caseWeight,
                               Comparable targetValue,
                               DataReaderCommand readerCommand)

Creates a lift chart, used to measure predictive performance.

Parameters:

actual - The name of the data column containing the actual classification.

predictedProbability - The name of the data column which contains the predicted probability generated by an inference query.

caseWeight - Optional case weight. Can be null.

targetValue - The target value of interest. E.g. True if we are interested in predicting Purchase=True.

readerCommand - The predictions.

Returns:

A lift chart.

getScore

public double getScore()

Gets the overall score, which is a positive or negative probability between 0 and 1 indicating the classification performance of the network.

Score = (area under predicted - area under random) / (area under ideal - area under random)

A score of 0 indicates that the predictive performance is no better than random.

A score of 1 indicates the network perfectly predicts all cases.

A negative score indicates the network performs worse than random, which may indicate incorrect usage.

getActual

public String getActual()

Gets the name of the data column containing the actual classification.

getIdeal

public double getIdeal()

Gets the population probability value at which the target reaches 100 %.

getPoints

public List<LiftChartPoint> getPoints()

Gets the xy points that make up the lift chart.

getPredictedProbability

public String getPredictedProbability()

Gets the name of the data column which contains the predicted probability generated by an inference query.

getTargetValue

public Comparable getTargetValue()

Gets the target value which we are interested in. For example, for a variable 'Purchase' with state values True and False, if we are trying to predict whether someone will make a purchase or not, the target value would be True.