Module datarush.analytics
Package 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.

  • Field Details

    • ELEM_THIS

      protected static final String ELEM_THIS
      Constant for the name of the PredictiveModelQuality element.
      See Also:

  • Constructor Details

    • PredictiveModelQuality

      public PredictiveModelQuality()
      Default constructor for bean-like behavior

  • Method Details

    • 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