GeneralRegressionModel
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package 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.
Returns true if there are any missing values of all input fields in the specified series.
Returns true if there are any missing values of all input fields in the specified series.
Common attributes of this model
Common attributes of this model
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.
The schema of candidate outputs.
The schema of candidate outputs.
Returns class labels of the specified target.
Returns class labels of the specified target.
The class labels in a classification model.
The class labels in a classification model.
Creates an object of GeneralRegressionOutputs that is for writing into an output series.
Creates an object of GeneralRegressionOutputs that is for writing into an output series.
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.
Returns PMML version as a double value
Returns PMML version as a double value
The data dictionary of this model.
The data dictionary of this model.
Returns all candidates output fields of this model when there is no output specified explicitly.
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.
Encodes the input series.
Encodes the input series.
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.
Returns the field of a given name.
Returns the field of a given name.
FieldNotFoundException if a field with the given name does not exist
Get fields by its usage type: 'active', 'target', 'predicted', 'group' and so on
Get fields by its usage type: 'active', 'target', 'predicted', 'group' and so on
Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.
Describe the kind of mining model, e.g., whether it is intended to be used for clustering or for classification.
Returns the field of a given name, None if a field with the given name does not exist.
The header of this model.
The header of this model.
Implicit referenced derived fields for the sub-model except ones defined in the mining schema.
Implicit referenced derived fields for the sub-model except ones defined in the mining schema.
Returns importances of predictors.
Returns importances of predictors.
The sub-classes can override this method to provide classes of target inside model.
The sub-classes can override this method to provide classes of target inside model.
Referenced derived fields.
Referenced derived fields.
All input fields in an array.
All input fields in an array.
All input names in an array.
All input names in an array.
The schema of inputs.
The schema of inputs.
Tests if this is a association rules model.
Tests if this is a association rules model.
Tests if the target is a binary field
Tests if the target is a binary field
Tests if this is a classification model of the specified target, it's applicable for multiple targets.
Tests if this is a classification model of the specified target, it's applicable for multiple targets.
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 the target is an ordinal field
Tests if the target is an ordinal field
Tests if this is a regression model of the specified target, it's applicable for multiple targets.
Tests if this is a regression model of the specified target, it's applicable for multiple targets.
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 optional local transformations.
The optional local transformations.
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.
Model element type.
Model element type.
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.
A series with all null values is returned when can not produce a result.
A series with all null values is returned when can not produce a result.
Returns the number of class labels of the specified target.
Returns the number of class labels of the specified target.
The number of class labels in a classification model.
The number of class labels in a classification model.
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.
Returns optype of the specified target.
Returns optype of the specified target.
When Target specifies optype then it overrides the optype attribute in a corresponding MiningField, if it exists.
When Target specifies optype then it overrides the optype attribute in a corresponding MiningField, if it exists. If the target does not specify optype then the MiningField is used as default. And, in turn, if the MiningField does not specify an optype, it is taken from the corresponding DataField. In other words, a MiningField overrides a DataField, and a Target overrides a MiningField.
The schema of final outputs.
The schema of final outputs.
The parent model.
The parent model.
Predicts values for a given data series.
Predicts values for a given data series.
Predicts one or multiple records in json format, there are two formats supported:
Predicts one or multiple records in json format, there are two formats supported:
- ‘records’ : list like [{column -> value}, … , {column -> value}] - ‘split’ : dict like {‘columns’ -> [columns], ‘data’ -> [values]}
Records in json
Results in json
Predicts values for a given Array, and the order of those values is supposed as same as the input fields list
Predicts values for a given Array, and the order of those values is supposed as same as the input fields list
Predicts values for a given list of key/value pairs.
Predicts values for a given list of key/value pairs.
Predicts values for a given data map of Java.
Predicts values for a given data map of Java.
Predicts values for a given data map.
Predicts values for a given data map.
Pre-process the input series.
Pre-process the input series.
Tests if probabilities of categories of target can be produced by this model.
Tests if probabilities of categories of target can be produced by this model.
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.
The class labels of all categorical targets.
The class labels of all categorical targets.
The first target field for the supervised model.
The first target field for the supervised model.
All target fields in an array.
All target fields in an array. Multiple target fields are allowed. It depends on the kind of the model whether prediction of multiple fields is supported.
Returns targets that are residual values to be computed, the input data must include target values.
Returns targets that are residual values to be computed, the input data must include target values.
Name of the first target for the supervised model.
Name of the first target for the supervised model.
All target names in an array.
All target names in an array.
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.
The optional transformation dictionary.
The optional transformation dictionary.
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.
Setup indices to retrieve data from series faster by index instead of name, the index is immutable when model is built because the model object could run in multiple threads, so it's important make sure the model object is totally immutable.
Setup indices to retrieve data from series faster by index instead of name, the index is immutable when model is built because the model object could run in multiple threads, so it's important make sure the model object is totally immutable.
Setup indices of targets that are usually not used by the scoring process, they are only used when residual values to be computed.
PMML version.
PMML version.
Definition of a general regression model. As the name says it, this is intended to support a multitude of regression models.