MiningModel
Related Doc:
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
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 MiningOutputs that is for writing into an output series.
Creates an object of MiningOutputs that is for writing into an output series.
User-defined custom output fields, both the internal output of PMML and predefined output are ignored when the field is specified.
User-defined custom output fields, both the internal output of PMML and predefined output are ignored when the field 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.
Returns all candidates output fields of this model when there is no output specified explicitly.
Encodes the input series.
Encodes the input series.
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.
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.
The optional local transformations.
The optional local transformations.
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.
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.
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.
A flag for whether to return those predefined output fields not exist in the output element explicitly.
A flag for whether to return those predefined output fields not exist in the output element explicitly.
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.
The optional transformation dictionary.
The optional transformation dictionary.
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.
The schema of used fields.
The schema of used fields.
PMML version.
PMML version.
The element MiningModel allows precise specification of the usage of multiple models within one PMML file. The two main approaches are Model Composition, and Segmentation.
Model Composition includes model sequencing and model selection but is only applicable to Tree and Regression models. Segmentation allows representation of different models for different data segments and also can be used for model ensembles and model sequences. Scoring a case using a model ensemble consists of scoring it using each model separately, then combining the results into a single scoring result using one of the pre-defined combination methods. Scoring a case using a sequence, or chain, of models allows the output of one model to be passed in as input to subsequent models.
ModelComposition uses "embedded model elements" that are defeatured copies of "standalone model elements" -- specifically, Regression for RegressionModel, DecisionTree for TreeModel. Besides being limited to Regression and Tree models, these embedded model elements lack key features like a MiningSchema (essential to manage scope across multiple model elements). Therefore, in PMML 4.2, the Model Composition approach has been deprecated since the Segmentation approach allows for a wider range of models to be used more reliably. For more on deprecation, see Conformance.
Segmentation is accomplished by using any PMML model element inside of a Segment element, which also contains a PREDICATE and an optional weight. MiningModel then contains Segmentation element with a number of Segment elements as well as the attribute multipleModelMethod specifying how all the models applicable to a record should be combined. It is also possible to use a combination of model composition and segmentation approaches, using simple regression or decision trees for data preprocessing before segmentation.