Common attributes of this model
Common attributes of this model
The algorithm name is free-type and can be any description for the specific algorithm that produced the model.
The algorithm name is free-type and can be any description for the specific algorithm that produced the model. This attribute is for information only.
If modelType is CoxRegression, this variable is optional, if present it is used during scoring (see the description of scoring procedures below).
If modelType is CoxRegression, this variable is optional, if present it is used during scoring (see the description of scoring procedures below). This attribute must refer to a DataField or a DerivedField containing a categorical variable.
Specifies the type of cumulative link function to use when ordinalMultinomial model type is specified.
Specifies the type of cumulative link function to use when ordinalMultinomial model type is specified.
Specifies an ancillary parameter value for the negative binomial distribution.
Specifies an ancillary parameter value for the negative binomial distribution.
The probability distribution of the dependent variable for generalizedLinear model may be specified as normal, binomial, gamma, inverse Gaussian, negative binomial, or Poisson.
The probability distribution of the dependent variable for generalizedLinear model may be specified as normal, binomial, gamma, inverse Gaussian, negative binomial, or Poisson. Note that binomial distribution can be used in two situations: either the target is categorical with two categories or a trialsVariable or trialsValue is specified.
If modelType is CoxRegression, this variable is required during scoring (see the description of scoring procedures below).
If modelType is CoxRegression, this variable is required during scoring (see the description of scoring procedures below). This attribute must refer to a DataField or a DerivedField containing a continuous variable.
Describe the kind of mining model, e.
Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.
Tests if this is a association rules model.
Tests if this is a association rules model.
Tests if this is a classification model.
Tests if this is a classification model.
Tests if this is a clustering model.
Tests if this is a clustering model.
Tests if this is a mixed model.
Tests if this is a mixed model.
Tests if this is a regression model.
Tests if this is a regression model.
Indicates if the model is valid for scoring.
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.
Tests if this is a sequences model.
Tests if this is a sequences model.
Tests if this is a time series model.
Tests if this is a time series model.
Specifies the type of link function to use when generalizedLinear model type is specified.
Specifies the type of link function to use when generalizedLinear model type is specified.
Specifies an additional number the following link functions need: oddspower and power.
Specifies an additional number the following link functions need: oddspower and power.
The value of degrees of freedom for the model.
The value of degrees of freedom for the model. This value is needed for computing confidence intervals for predicted values.
Identifies the model with a unique name in the context of the PMML file.
Identifies the model with a unique name in the context of the PMML file. This attribute is not required. Consumers of PMML models are free to manage the names of the models at their discretion.
Specifies the type of regression model in use.
Specifies the type of regression model in use.
If present, this value is used during scoring generalizedLinear, ordinalMultinomial, or multinomialLogistic models.
If present, this value is used during scoring generalizedLinear, ordinalMultinomial, or multinomialLogistic models. It works like a user-specified intercept (see the description of the scoring procedures below). At most one of the attributes offsetVariable and offsetValue can be present in a model.
If present, this variable is used during scoring generalizedLinear, ordinalMultinomial, or multinomialLogistic models (see the description of scoring procedures below).
If present, this variable is used during scoring generalizedLinear, ordinalMultinomial, or multinomialLogistic models (see the description of scoring procedures below). This attribute must refer to a DataField or a DerivedField.
If modelType is CoxRegression, this variable is optional, it is not used during scoring but is an important piece of information about model building.
If modelType is CoxRegression, this variable is optional, it is not used during scoring but is an important piece of information about model building. This attribute must refer to a DataField or a DerivedField containing a continuous variable.
If modelType is CoxRegression, this variable is optional.
If modelType is CoxRegression, this variable is optional. This attribute must refer to a DataField or a DerivedField.
If modelType is CoxRegression, this variable is optional, it is not used during scoring but is an important piece of information about model building.
If modelType is CoxRegression, this variable is optional, it is not used during scoring but is an important piece of information about model building. This attribute must refer to a DataField or a DerivedField. Explicitly listing all categories of this variable is not recommended.
Used for specifying the reference category of the target variable in a multinomial classification model.
Used for specifying the reference category of the target variable in a multinomial classification model. Normally the reference category is the one from DataDictionary that does not appear in the ParamMatrix, but when several models are combined in one PMML file an explicit specification is needed.
Name of the target variable (also called response variable).
Name of the target variable (also called response variable). This attribute has been deprecated since PMML 3.0. If present, it should match the name of the target MiningField.
A positive integer used during scoring some generalizedLinear models (see the description of scoring procedure below).
A positive integer used during scoring some generalizedLinear models (see the description of scoring procedure below). At most one of the attributes trialsVariable and trialsValue can be present in a model. This attribute can only be used when the distribution is binomial.
Specifies an additional variable used during scoring some generalizedLinear models (see the description of scoring procedure below).
Specifies an additional variable used during scoring some generalizedLinear models (see the description of scoring procedure below). This attribute must refer to a DataField or a DerivedField. This attribute can only be used when the distribution is binomial.