com.pervasive.datarush.analytics.pmml

Class PredictiveModelQuality

java.lang.Object

com.pervasive.datarush.analytics.pmml.ModelQuality

com.pervasive.datarush.analytics.pmml.PredictiveModelQuality

public class PredictiveModelQuality
extends ModelQuality

A PMML object model for some of the metadata about a predictive (usually regression) model's quality.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	PredictiveModelQuality.Usage Indicator for the phases of model-building during which a dataset may be used.

Field Summary

Fields

Modifier and Type	Field and Description
protected static String	ELEM_THIS Constant for the name of the PredictiveModelQuality element.

Fields inherited from class com.pervasive.datarush.analytics.pmml.ModelQuality

ATT_DATA_NAME

Constructor Summary

Constructors

Constructor and Description
PredictiveModelQuality() Default constructor for bean-like behavior

Method Summary

Modifier and Type	Method and Description
Double	getAIC() Get the Akaike Information Criterion, a measure of the relative goodness of fit of a statistical model.
Double	getBIC() Get the Bayesian Information Criterion, a measure of the relative goodness of fit of a statistical model which penalizes the number of parameters more strongly than AIC.
PredictiveModelQuality.Usage	getDataUsage() Get the dataUsage, the relationship between the model and the dataset used to measure its quality.
Integer	getDegreesOfFreedom() Get the Degrees of Freedom of the error of the model
double	getExtensionStatistic(String name) Retrieve a statistic about the model that is not directly supported by PMML
Set<String>	getExtensionStatisticNames()
Double	getMeanAbsoluteError() Get the meanAbsoluteError, the mean of the absolute values of the predictive errors on that dataset.
Double	getMeanSquaredError() Get the Mean Squared Error, the mean of the squares of the predictive errors on that dataset.
Double	getR_squared() Get the r-squared attribute, a measure of the amount of variance in the target variable explained by a model.
Double	getRootMeanSquaredError() Get the rootMeanSquaredError, the square root of the mean of the squares of the predictive errors on the dataset.
Double	getSumSquaredError() Get the Sum Of Squares (Error) statistic.
Double	getSumSquaredRegression() Get the Sum Of Squares (Regression) statistic.
String	getTargetField() Get the predicted field on which the quality information was measured.
boolean	hasExtensionStatistic(String name) Check whether a certain statistic is available as an extension
protected void	parse(Element toParse) Initialize this object's state from a PMML element with the appropriate type
void	setAIC(Double AIC)
void	setBIC(Double BIC)
void	setDataUsage(PredictiveModelQuality.Usage dataUsage) Sets the "dataUsage" attribute for the model.
void	setDegreesOfFreedom(Integer degreesOfFreedom)
void	setExtensionStatistic(String name, double value) Sets the value of an attribute not directly supported by PMML.
void	setMeanAbsoluteError(Double meanAbsoluteError) Sets the "meanAbsoluteError" attribute for the model.
void	setMeanSquaredError(Double meanSquaredError) Sets the "meanSquaredError" attribute for the model.
void	setR_squared(Double r_squared) Sets the "r-squared" attribute for the model.
void	setRootMeanSquaredError(Double rootMeanSquaredError)
void	setSumSquaredError(Double sumSquaredError)
void	setSumSquaredRegression(Double sumSquaredRegression)
void	setTargetField(String targetField) Sets the "targetField" attribute for the model.
void	toPMML(Element parent) Build this ModelQuality as a PMML element with a given parent

Methods inherited from class com.pervasive.datarush.analytics.pmml.ModelQuality

getDataName, setDataName, smartParse

Methods inherited from class java.lang.Object

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

Field Detail

ELEM_THIS

protected static final String ELEM_THIS

Constant for the name of the PredictiveModelQuality element.

See Also:

Constant Field Values

Constructor Detail

PredictiveModelQuality

public PredictiveModelQuality()

Default constructor for bean-like behavior

Method Detail

toPMML

public void toPMML(Element parent)

Description copied from class: ModelQuality

Build this ModelQuality as a PMML element with a given parent

Specified by:

toPMML in class ModelQuality

Parameters:

parent - the future parent of the element

parse

protected void parse(Element toParse)

Description copied from class: ModelQuality

Initialize this object's state from a PMML element with the appropriate type

Specified by:

parse in class ModelQuality

Parameters:

toParse - The element with the state

getR_squared

public Double getR_squared()

Get the r-squared attribute, a measure of the amount of variance in the target variable explained by a model. It ranges from 0.0 (the model explains nothing) to 1.0 (the model is a perfect predictor).

Returns:

the "r-squared" attribute for the model, or null if it is not included.

getMeanAbsoluteError

public Double getMeanAbsoluteError()

Get the meanAbsoluteError, the mean of the absolute values of the predictive errors on that dataset.

Returns:

the "meanAbsoluteError" attribute for the model, or null if it is not included.

getRootMeanSquaredError

public Double getRootMeanSquaredError()

Get the rootMeanSquaredError, the square root of the mean of the squares of the predictive errors on the dataset.

Returns:

the "rootMeanSquaredError" attribute for the model, or null if it is not included.

getAIC

public Double getAIC()

Get the Akaike Information Criterion, a measure of the relative goodness of fit of a statistical model.

Returns:

the "AIC" attribute for the model, or null if it is not included.

getBIC

public Double getBIC()

Get the Bayesian Information Criterion, a measure of the relative goodness of fit of a statistical model which penalizes the number of parameters more strongly than AIC.

Returns:

the "BIC" attribute for the model, or null if it is not included.

getDegreesOfFreedom

public Integer getDegreesOfFreedom()

Get the Degrees of Freedom of the error of the model

Returns:

the "degreesOfFreedom" attribute for the model, or null if it is not included.

getMeanSquaredError

public Double getMeanSquaredError()

Get the Mean Squared Error, the mean of the squares of the predictive errors on that dataset.

Returns:

the "meanSquaredeError" attribute for the model-quality element, or null if it is not included.

getSumSquaredError

public Double getSumSquaredError()

Get the Sum Of Squares (Error) statistic.

Returns:

the "sumSquaredError" attribute for the model-quality element, or null if it is not included.

getSumSquaredRegression

public Double getSumSquaredRegression()

Get the Sum Of Squares (Regression) statistic.

Returns:

the "sumSquaredRegression" attribute for the model-quality element, or null if it is not included.

getDataUsage

public PredictiveModelQuality.Usage getDataUsage()

Get the dataUsage, the relationship between the model and the dataset used to measure its quality. It indicates the phase of model-building during which the model was first exposed to data from that set.

Returns:

the "dataUsage" attribute for the model-quality element.

getTargetField

public String getTargetField()

Get the predicted field on which the quality information was measured.

Returns:

the "dataUsage" attribute for the model-quality element.

hasExtensionStatistic

public boolean hasExtensionStatistic(String name)

Check whether a certain statistic is available as an extension

Parameters:

name - The name of the statistic to check

Returns:

true iff the statistic is present and available.

getExtensionStatistic

public double getExtensionStatistic(String name)

Retrieve a statistic about the model that is not directly supported by PMML

Parameters:

name - the extension statistic to check

Returns:

the value of the named extension statistic, or Double.NaN if the extension is not present.

getExtensionStatisticNames

public Set<String> getExtensionStatisticNames()

Returns:

a collection of Strings which, when passed to hasExtensionStatistic, return true

setTargetField

public void setTargetField(String targetField)

Sets the "targetField" attribute for the model.

Parameters:

targetField - the "targetField" attribute for the model.

setR_squared

public void setR_squared(Double r_squared)

Sets the "r-squared" attribute for the model. If null, the attribute will be absent.

Parameters:

r_squared - the "r-squared" attribute for the model.

setMeanAbsoluteError

public void setMeanAbsoluteError(Double meanAbsoluteError)

Sets the "meanAbsoluteError" attribute for the model. If null, the attribute will be absent.

Parameters:

meanAbsoluteError - the "meanAbsoluteError" attribute for the model.

setMeanSquaredError

public void setMeanSquaredError(Double meanSquaredError)

Sets the "meanSquaredError" attribute for the model. If null, the attribute will be absent.

Parameters:

meanSquaredError - the "meanSquaredError" attribute for the model.

setRootMeanSquaredError

public void setRootMeanSquaredError(Double rootMeanSquaredError)

setAIC

public void setAIC(Double AIC)

setBIC

public void setBIC(Double BIC)

setDegreesOfFreedom

public void setDegreesOfFreedom(Integer degreesOfFreedom)

setSumSquaredError

public void setSumSquaredError(Double sumSquaredError)

setSumSquaredRegression

public void setSumSquaredRegression(Double sumSquaredRegression)

setDataUsage

public void setDataUsage(PredictiveModelQuality.Usage dataUsage)

Sets the "dataUsage" attribute for the model. If null, the attribute will be set to "training", the default.

Parameters:

dataUsage - the "dataUsage" attribute for the model.

setExtensionStatistic

public void setExtensionStatistic(String name,
                                  double value)

Sets the value of an attribute not directly supported by PMML. It will appear as an Extension element instead of as an attribute.

Parameters:

name - the "name" attribute of the extension

value - the "value" attribute of the extension