PMML 4.4 - Regression

The regression functions are used to determine the relationship between the dependent variable (target field) and one or more independent variables. The dependent variable is the one whose values you want to predict, whereas the independent variables are the variables that you base your prediction on. While the term regression usually refers to the prediction of numeric values, the PMML element RegressionModel can also be used for classification. This is due to the fact that multiple regression equations can be combined in order to predict categorical values.

If the attribute functionName is regression then the model is used for the prediction of a numeric value in a continuous domain. These models should contain exactly one regression table.

If the attribute functionName is classification then the model is used to predict a category. These models should contain exactly one regression table for each targetCategory. The normalizationMethod describes how the prediction is converted into a confidence value (aka probability).

For simple regression with functionName='regression', the formula is:

Dependent variable = intercept + Sum_i (coefficient_i * independent variable_i ) + error

Classification models can have multiple regression equations. With n classes/categories there are n equations of the form

y_j = intercept_j + Sum_i (coefficient_j_i * independent variable_i )

One method to compute the confidence/probability value for category j is to use the softmax function

p_j = exp(y_j) / (Sum[i in 1..n](exp(y_i)) )

Another method, called simplemax, uses a simple quotient

p_j = y_j / (Sum[i in 1..n](y_i) )

These confidence values are similar to statistical probabilities but they only mimic probabilities by post-processing the values y_i.

Note that Binary logistic regression is a special case of softmax with

y = intercept + Sum_i (coefficient_i * independent variable_i )
p = 1/(1+exp(-y))

It should be implemented as a classification model

<RegressionModel functionName="classification"  normalizationMethod="softmax" ...
  <RegressionTable targetCategory="YES" ...
  <RegressionTable targetCategory="NO" intercept="0.0"
  ...

Here p will be the probability associated with YES, the first targetCategory. Note that the probability of YES, as given by the softmax normalization method, is

p=exp(y)/(exp(y)+exp(0.0)).

which is equivalent to

p=(1/(1+exp(-y))

The probability of NO is then computed as 1-p.

The XML Schema for RegressionModel

<xs:element name="RegressionModel">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element ref="MiningSchema"/>
      <xs:element ref="Output" minOccurs="0"/>
      <xs:element ref="ModelStats" minOccurs="0"/>
      <xs:element ref="ModelExplanation" minOccurs="0"/>
      <xs:element ref="Targets" minOccurs="0"/>
      <xs:element ref="LocalTransformations" minOccurs="0"/>
      <xs:element ref="RegressionTable" maxOccurs="unbounded"/>
      <xs:element ref="ModelVerification" minOccurs="0"/>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="modelName" type="xs:string"/>
    <xs:attribute name="functionName" type="MINING-FUNCTION" use="required"/>
    <xs:attribute name="algorithmName" type="xs:string"/>
    <xs:attribute name="modelType" use="optional">
      <xs:simpleType>
        <xs:restriction base="xs:string">
          <xs:enumeration value="linearRegression"/>
          <xs:enumeration value="stepwisePolynomialRegression"/>
          <xs:enumeration value="logisticRegression"/>
        </xs:restriction>
      </xs:simpleType>
    </xs:attribute>
    <xs:attribute name="targetFieldName" type="FIELD-NAME" use="optional"/>
    <xs:attribute name="normalizationMethod" type="REGRESSIONNORMALIZATIONMETHOD" default="none"/>
    <xs:attribute name="isScorable" type="xs:boolean" default="true"/>
  </xs:complexType>
</xs:element>

<xs:simpleType name="REGRESSIONNORMALIZATIONMETHOD">
  <xs:restriction base="xs:string">
    <xs:enumeration value="none"/>
    <xs:enumeration value="simplemax"/>
    <xs:enumeration value="softmax"/>
    <xs:enumeration value="logit"/>
    <xs:enumeration value="probit"/>
    <xs:enumeration value="cloglog"/>
    <xs:enumeration value="exp"/>
    <xs:enumeration value="loglog"/>
    <xs:enumeration value="cauchit"/>
  </xs:restriction>
</xs:simpleType>

Definitions

RegressionModel: The root element of an XML regression model. Each instance of a regression model must start with this element.
modelName: This is a unique identifier specifying the name of the regression model.

functionName: Can be regression or classification.

algorithmName: Can be any string describing the algorithm that was used while creating the model.

modelType: Specifies the type of a regression model. The attribute modelType is for information only. It has been changed to optional and the usage is deprecated. Use functionName and normalizationMethod in order to define the computation. Use algorithmName in order to give further optional information.

targetFieldName: The name of the target field (also called dependent variable). The attribute targetFieldName is for information only. It has been changed to optional and the usage is deprecated. Use usageType="target" in MiningField instead.

isScorable: This attribute indicates if the model is valid for scoring. If this attribute is true or if it is missing, then the model should be processed normally. However, if the attribute is false, then the model producer has indicated that this model is intended for information purposes only and should not be used to generate results. In order to be valid PMML, all required elements and attributes must be present, even for non-scoring models. For more details, see General Structure.

<xs:element name="RegressionTable">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element ref="NumericPredictor" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element ref="CategoricalPredictor" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element ref="PredictorTerm" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="intercept" type="REAL-NUMBER" use="required"/>
    <xs:attribute name="targetCategory" type="xs:string"/>
  </xs:complexType>
</xs:element>

RegressionTable: A table that lists the values of all predictors or independent variables. If the model is used to predict a numerical field, then there is only one RegressionTable and the attribute targetCategory may be missing. If the model is used to predict a categorical field, then there are two or more RegressionTables and each one must have the attribute targetCategory defined with a unique value.

<xs:element name="NumericPredictor">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="name" type="FIELD-NAME" use="required"/>
    <xs:attribute name="exponent" type="INT-NUMBER" default="1"/>
    <xs:attribute name="coefficient" type="REAL-NUMBER" use="required"/>
  </xs:complexType>
</xs:element>

<xs:element name="CategoricalPredictor">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="name" type="FIELD-NAME" use="required"/>
    <xs:attribute name="value" type="xs:string" use="required"/>
    <xs:attribute name="coefficient" type="REAL-NUMBER" use="required"/>
  </xs:complexType>
</xs:element>

<xs:element name="PredictorTerm">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element ref="FieldRef" minOccurs="1" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="name" type="FIELD-NAME"/>
    <xs:attribute name="coefficient" type="REAL-NUMBER" use="required"/>
  </xs:complexType>
</xs:element>

NumericPredictor: Defines a numeric independent variable. The list of valid attributes comprises the name of the variable, the exponent to be used, and the coefficient by which the values of this variable must be multiplied. Note that the exponent defaults to 1, hence it is not always necessary to specify. Also, if the input value is missing, the result evaluates to a missing value.

CategoricalPredictor: Defines a categorical independent variable. The list of attributes comprises the name of the variable, the value attribute, and the coefficient by which the values of this variable must be multiplied.
To do a regression analysis with categorical values, some means must be applied to enable calculations. If the specified value of an independent value occurs, the term variable_name(value) is replaced with 1. Thus the coefficient is multiplied by 1. If the value does not occur, the term variable_name(value) is replaced with 0 so that the product coefficient × variable_name(value) yields 0. Consequently, the product is ignored in the ongoing analysis. If the input value is missing then variable_name(v) yields 0 for any v.

PredictorTerm: Contains one or more fields that are combined by multiplication. That is, this element supports interaction terms. The type of all fields referenced within PredictorTerm must be continuous. Note that if the input value is missing, the result evaluates to a missing value. The name attribute allows this term to be referenced by elements of Statistics and should be unique from any other field names with the scope of this RegressionModel.
The content of PredictorTerm might be extended to a sequence of any expression. This feature is not yet needed.

Valid combinations

functionName	normalizationMethod	number of RegressionTable elements	result
`regression`	`none`	1	predictedValue = y₁
`regression`	`softmax,logit`	1	predictedValue = 1/(1+exp(-y₁))
`regression`	`exp`	1	predictedValue = exp(y₁)
`regression`	`probit`	1	predictedValue = CDF(y₁)
`regression`	`cloglog`	1	predictedValue = 1 - exp( -exp( y₁))
`regression`	`loglog`	1	predictedValue = exp( -exp( -y₁))
`regression`	`cauchit`	1	predictedValue = 0.5 + (1/π) arctan(y₁)
`regression`	other	1	ERROR
`regression`	any	>1	ERROR
`classification` with an ordinal target	`logit, probit, cauchit, cloglog, loglog, none`	>1	apply norm.method to y₁ .. y_n to find cumulative probabilities, see text for details
`classification` with a binary target	`logit, probit, cauchit, cloglog, loglog`	2	apply norm.method to y₁, p₂=1-p₁
`classification` with a binary target	`none`	2	if y₁<0 p₁=0, if y₁>1 p₁=1, else p₁=y₁. p₂=1-p₁
`classification` with a categorical target with 2 or more categories	`softmax,simplemax`	2 or more	apply norm.method to y₁ .. y_n
`classification` with a categorical target with >2 categories	`none`	>2	p_i=y_i for i=1..n-1, p_n=1-Sum(p_i)
`classification`	other	any	ERROR

How to compute `p_j` := probability of target=`Value_j`

Let there be N target categories with N RegressionTable elements. Let y_j be the result of evaluating the formula in the jth RegressionTable. If one or more of the y_j cannot be evaluated because the value in one of the referenced fields is missing, then the following formulas do not apply. In that case the predictions are defined by the priorProbability values in the Target element if present, undefined otherwise.

softmax, categorical: p_j = exp(y_j) / ( Sum[i = 1 to N](exp(y_i) ) )
simplemax, categorical: p_j = y_j / ( Sum[i = 1 to N ]( y_i ) )
none, categorical: p_j = y_j for j = 1 to N - 1, p_N = 1 - Sum[ i = 1 to N - 1 ]( p_i )
Note that if N=2 and the probabilities are outside of [0,1] they should be changed to the closest 0 or 1.
logit, categorical with two categories: p₁ = 1 / ( 1 + exp( -y₁ ) ), p₂ = 1 - p₁
probit, categorical with two categories: p₁ = integral(from -∞ to y₁)(1/sqrt(2*π))exp(-0.5*u*u)du, p₂ = 1 - p₁
cloglog, categorical with two categories: p₁ = 1 - exp( -exp( y₁ ) ), p₂ = 1 - p₁
loglog, categorical with two categories: p₁ = exp( -exp( -y₁ ) ), p₂ = 1 - p₁
cauchit, categorical with two categories: p₁ = 0.5 + (1/π) arctan( y₁ ), p₂ = 1 - p₁
logit, ordinal: inverse of logit function: F(y)= 1/(1+exp(-y)), e.g. F(15) = 1 p₁ = F(y₁) p_j = F(y_j) - F(y_j-1), for 2 ≤ j < N p_N = 1 - F(y_N-1)
probit, ordinal: inverse of probit function: F(y)= integral(from -∞ to y)(1/sqrt(2*π))exp(-0.5*u*u)du e.g. F(10) = 1 p₁ = F(y₁) p_j = F(y_j) - F(y_j-1), for 2 ≤ j < N p_N = 1 - F(y_N-1)
cloglog, ordinal: inverse of cloglog function: F(y)= 1 - exp( -exp(y) ) e.g. F(4) = 1. p₁ = F(y₁) p_j = F(y_j) - F(y_j-1), for 2 ≤ j < N p_N = 1 - F(y_N-1)
loglog, ordinal: inverse of cloglog function: F(y) = exp( -exp(-y) ) p₁ = F(y₁) p_j = F(y_j) - F(y_j-1), for 2 ≤ j < N p_N = 1 - F(y_N-1)
cauchit, ordinal: inverse of cauchit function: F(y) = 0.5 + (1/π) arctan(y) p₁ = F(y₁) p_j = F(y_j) - F(y_j-1), for 2 ≤ j < N p_N = 1 - F(y_N-1)
none, ordinal: p₁ = y₁ p_j = y_j - y_j-1, for 2 ≤ j < N p_N = 1 - y_N-1

Comments on exp: The exp normalizationMethod is frequently used in statistical models for predicting non-negative target variables, such as Poisson regression which is used in sales forecasting, queuing models, insurance risk models, etc., to predict counts per unit time (e.g., daily sales volumes, hourly service requests, quarterly insurance claim filings, etc.).

Comments on probit: The area under the standard normal curve corresponds to probability, specifically the probability of finding an observation less than a given Z value. The total area under the curve, from -∞ to +∞ = 1.0

Z = 0, area = above Z = 0.5, ie half the curve lies below the mean
Z = 1.0, area = above Z =0.1587, ie about 85% data lies below (mean + 1 sd)

t	F(`t`)
1	0.84134474606854000000
2	0.97724986805182000000
3	0.99865010196837000000

For ordinal targets: Suppose y_i is the result of the ith RegressionTable. Apply the inverse link function to obtain the cumulative probability. For the last (which is the so-called trivial) RegressionTable, the intercept should be a "large" number so that after applying the inverse link function, you obtain a cumulative probability of 1; this is assumed above in the equations given on how to compute the probabilities of ordinal targets. The individual probability of each category is calculated by subtracting the cumulative probability of the previous category from the cumulative probability of the current category.

Examples

The following regression formula is used to predict the number of insurance claims:

number_of_claims =

132.37 + 7.1*age + 0.01*salary + 41.1*car_location('carpark') + 325.03*car_location('street')

If the value carpark was specified for car_location in a particular record, you would get the following formula:

number_of_claims = 132.37 + 7.1 age + 0.01 salary + 41.1 * 1 + 325.03 * 0

Linear Regression Sample

This is a linear regression equation predicting a number of insurance claims on prior knowledge of the values of the independent variables age, salary and car_location. car_location is the only categorical variable. Its value attribute can take on two possible values, carpark and street.

number_of_claims = 132.37 + 7.1 age + 0.01 salary + 41.1 car_location( carpark ) + 325.03 car_location( street )

The corresponding PMML model is:

<PMML xmlns="https://www.dmg.org/PMML-4_4" version="4.4">
  <Header copyright="DMG.org"/>
  <DataDictionary numberOfFields="4">
    <DataField name="age" optype="continuous" dataType="double"/>
    <DataField name="salary" optype="continuous" dataType="double"/>
    <DataField name="car_location" optype="categorical" dataType="string">
      <Value value="carpark"/>
      <Value value="street"/>
    </DataField>
    <DataField name="number_of_claims" optype="continuous" dataType="integer"/>
  </DataDictionary>
  <RegressionModel modelName="Sample for linear regression" functionName="regression" algorithmName="linearRegression" targetFieldName="number_of_claims">
    <MiningSchema>
      <MiningField name="age"/>
      <MiningField name="salary"/>
      <MiningField name="car_location"/>
      <MiningField name="number_of_claims" usageType="target"/>
    </MiningSchema>
    <RegressionTable intercept="132.37">
      <NumericPredictor name="age" exponent="1" coefficient="7.1"/>
      <NumericPredictor name="salary" exponent="1" coefficient="0.01"/>
      <CategoricalPredictor name="car_location" value="carpark" coefficient="41.1"/>
      <CategoricalPredictor name="car_location" value="street" coefficient="325.03"/>
    </RegressionTable>
  </RegressionModel>
</PMML>

Polynomial Regression Sample

This is a polynomial regression equation predicting a number of insurance claims on prior knowledge of the values of the independent variables salary and car_location. car_location is a categorical variable. Its value attribute can take on two possible values, carpark and street.

number_of_claims =

3216.38 - 0.08 salary + 9.54E-7 salary**2 - 2.67E-12 salary**3 + 93.78 car_location('carpark') + 288.75 car_location('street')

<PMML xmlns="https://www.dmg.org/PMML-4_4" version="4.4">
  <Header copyright="DMG.org"/>
  <DataDictionary numberOfFields="3">
    <DataField name="salary" optype="continuous" dataType="double"/>
    <DataField name="car_location" optype="categorical" dataType="string">
      <Value value="carpark"/>
      <Value value="street"/>
    </DataField>
    <DataField name="number_of_claims" optype="continuous" dataType="integer"/>
  </DataDictionary>
  <RegressionModel functionName="regression" modelName="Sample for stepwise polynomial regression" algorithmName="stepwisePolynomialRegression" targetFieldName="number_of_claims">
    <MiningSchema>
      <MiningField name="salary"/>
      <MiningField name="car_location"/>
      <MiningField name="number_of_claims" usageType="target"/>
    </MiningSchema>
    <RegressionTable intercept="3216.38">
      <NumericPredictor name="salary" exponent="1" coefficient="-0.08"/>
      <NumericPredictor name="salary" exponent="2" coefficient="9.54E-7"/>
      <NumericPredictor name="salary" exponent="3" coefficient="-2.67E-12"/>
      <CategoricalPredictor name="car_location" value="carpark" coefficient="93.78"/>
      <CategoricalPredictor name="car_location" value="street" coefficient="288.75"/>
    </RegressionTable>
  </RegressionModel>
</PMML>

Logistic Regression for binary classification

Many regression modeling algorithms create (k-1) equations for classification problems with k different categories. This is particularly useful for binary classification. The resulting model can easily be defined in PMML as in the following example.

<PMML xmlns="https://www.dmg.org/PMML-4_4" version="4.4">
  <Header copyright="DMG.org"/>
  <DataDictionary numberOfFields="3">
    <DataField name="x1" optype="continuous" dataType="double"/>
    <DataField name="x2" optype="continuous" dataType="double"/>
    <DataField name="y" optype="categorical" dataType="string">
      <Value value="yes"/>
      <Value value="no"/>
    </DataField>
  </DataDictionary>
  <RegressionModel functionName="classification" modelName="Sample for stepwise polynomial regression" algorithmName="stepwisePolynomialRegression" normalizationMethod="softmax" targetFieldName="y">
    <MiningSchema>
      <MiningField name="x1"/>
      <MiningField name="x2"/>
      <MiningField name="y" usageType="target"/>
    </MiningSchema>
    <RegressionTable targetCategory="no" intercept="125.56601826">
      <NumericPredictor name="x1" coefficient="-28.6617384"/>
      <NumericPredictor name="x2" coefficient="-20.42027426"/>
    </RegressionTable>
    <RegressionTable targetCategory="yes" intercept="0"/>
  </RegressionModel>
</PMML>

Note that the last element for RegressionTable is trivial. It does not have any predictor entries. A RegressionTable defines a formula: intercept + (sum of predictor terms). If there are no predictor terms, then the (sum of ..) is 0 and the formula becomes just: intercept. That's exactly what <RegressionTable targetCategory="yes" intercept="0"/> defines.

Sample for classification with more than two categories:

y_clerical =

46.418 -0.132*age +7.867E-02*work -20.525*sex('0') +0*sex('1') -19.054*minority('0') +0*minority('1')

y_professional =

51.169 -0.302*age +.155*work -21.389*sex('0') +0*sex('1') -18.443*minority('0') + 0*minority('1')

y_trainee =

25.478 -.154*age +.266*work -2.639*sex('0') +0*sex('1') -19.821*minority('0') +0*minority('1')

Note that the terms such as 0*minority('1') are superfluous but it's valid to use the same field with different indicator values such as '0' and '1'. Though, a RegressionTable must not have multiple NumericPredictors with the same name and it must not have multiple CategoricalPredictors with the same pair of name and value.

The corresponding PMML model is:

<PMML xmlns="https://www.dmg.org/PMML-4_4" version="4.4">
  <Header copyright="DMG.org"/>
  <DataDictionary numberOfFields="5">
    <DataField name="age" optype="continuous" dataType="double"/>
    <DataField name="work" optype="continuous" dataType="double"/>
    <DataField name="sex" optype="categorical" dataType="string">
      <Value value="0"/>
      <Value value="1"/>
    </DataField>
    <DataField name="minority" optype="categorical" dataType="integer">
      <Value value="0"/>
      <Value value="1"/>
    </DataField>
    <DataField name="jobcat" optype="categorical" dataType="string">
      <Value value="clerical"/>
      <Value value="professional"/>
      <Value value="trainee"/>
      <Value value="skilled"/>
    </DataField>
  </DataDictionary>
  <RegressionModel modelName="Sample for logistic regression" functionName="classification" algorithmName="logisticRegression" normalizationMethod="softmax" targetFieldName="jobcat">
    <MiningSchema>
      <MiningField name="age"/>
      <MiningField name="work"/>
      <MiningField name="sex"/>
      <MiningField name="minority"/>
      <MiningField name="jobcat" usageType="target"/>
    </MiningSchema>
    <RegressionTable intercept="46.418" targetCategory="clerical">
      <NumericPredictor name="age" exponent="1" coefficient="-0.132"/>
      <NumericPredictor name="work" exponent="1" coefficient="7.867E-02"/>
      <CategoricalPredictor name="sex" value="0" coefficient="-20.525"/>
      <CategoricalPredictor name="sex" value="1" coefficient="0.5"/>
      <CategoricalPredictor name="minority" value="0" coefficient="-19.054"/>
      <CategoricalPredictor name="minority" value="1" coefficient="0"/>
    </RegressionTable>
    <RegressionTable intercept="51.169" targetCategory="professional">
      <NumericPredictor name="age" exponent="1" coefficient="-0.302"/>
      <NumericPredictor name="work" exponent="1" coefficient="0.155"/>
      <CategoricalPredictor name="sex" value="0" coefficient="-21.389"/>
      <CategoricalPredictor name="sex" value="1" coefficient="0.1"/>
      <CategoricalPredictor name="minority" value="0" coefficient="-18.443"/>
      <CategoricalPredictor name="minority" value="1" coefficient="0"/>
    </RegressionTable>
    <RegressionTable intercept="25.478" targetCategory="trainee">
      <NumericPredictor name="age" exponent="1" coefficient="-0.154"/>
      <NumericPredictor name="work" exponent="1" coefficient="0.266"/>
      <CategoricalPredictor name="sex" value="0" coefficient="-2.639"/>
      <CategoricalPredictor name="sex" value="1" coefficient="0.8"/>
      <CategoricalPredictor name="minority" value="0" coefficient="-19.821"/>
      <CategoricalPredictor name="minority" value="1" coefficient="0.2"/>
    </RegressionTable>
    <RegressionTable intercept="0.0" targetCategory="skilled"/>
  </RegressionModel>
</PMML>

Using interaction terms

The following example uses predictor terms that are implicitly combined by multiplication, aka interaction terms.

y =

2.1 -0.1* age *work -20.525*sex('0')

The corresponding PMML model is:

<PMML xmlns="https://www.dmg.org/PMML-4_4" version="4.4">
  <Header copyright="DMG.org"/>
  <DataDictionary numberOfFields="4">
    <DataField name="age" optype="continuous" dataType="double"/>
    <DataField name="work" optype="continuous" dataType="double"/>
    <DataField name="sex" optype="categorical" dataType="string">
      <Value value="male"/>
      <Value value="female"/>
    </DataField>
    <DataField name="y" optype="continuous" dataType="double"/>
  </DataDictionary>
  <RegressionModel modelName="Sample for interaction terms" functionName="regression" targetFieldName="y">
    <MiningSchema> 
      <MiningField name="age" optype="continuous"/> 
      <MiningField name="work" optype="continuous"/> 
      <MiningField name="sex" optype="categorical"/> 
      <MiningField name="y" optype="continuous" usageType="target"/> 
    </MiningSchema>
    <RegressionTable intercept="2.1">
      <CategoricalPredictor name="sex" value="female" coefficient="-20.525"/>
      <PredictorTerm coefficient="-0.1">
        <FieldRef field="age"/>
        <FieldRef field="work"/>
      </PredictorTerm>
    </RegressionTable>
  </RegressionModel>
</PMML>

Note that the model can convert the categorical field sex into a continuous field by defining an appropriate DerivedField. Furthermore, fields can appear more than once within a PredictorTerm.

For example,

(3.14 * salary² * age * income * (sex=='female'))

can be written in PMML as

<PredictorTerm coefficient="3.14">
  <FieldRef field="salary"/>
  <FieldRef field="age"/>
  <FieldRef field="income"/>
  <FieldRef field="salary"/>
  <FieldRef field="_g_0"/>  <!-- derived field for sex=='female' -->
</PredictorTerm>

e-mail

info at dmg.org