Model

Creates an object of subclass of ModelOutputs that is for writing into an output series.

Model element type.

Predicts values for a given data series.

The schema of candidate outputs.

Returns class labels of the specified target.

The data dictionary of this model.

Returns all candidates output fields of this model when there is no output specified explicitly.

Get fields by its usage type: 'active', 'target', 'predicted', 'group' and so on

Returns the field of a given name, None if a field with the given name does not exist.

The header of this model.

Returns importances of predictors.

The sub-classes can override this method to provide classes of target inside model.

Tests if the target is a binary field

Tests if this is a classification model.

Tests if this is a classification model of the specified target, it's applicable for multiple targets.

Tests if the target is an ordinal field

Tests if this is a regression model.

Tests if this is a regression model of the specified target, it's applicable for multiple targets.

Returns the number of class labels of the specified target.

Returns optype of the specified target.

The schema of final outputs.

Predicts values for a given data map.

Predicts values for a given data map of Java.

Predicts values for a given list of key/value pairs.

Predicts values for a given Array, and the order of those values is supposed as same as the input fields list

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]}

Tests if probabilities of categories of target can be produced by this model.

The optional transformation dictionary.

Common attributes of this model

Returns PMML version as a double value

Returns the field of a given name.

Tests if this is a association rules model.

Tests if this is a clustering model.

Tests if this is a mixed model.

Tests if this is a sequences model.

Tests if this is a time series model.

The optional local transformations.

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.

PMML version.

The class labels in a classification model.

Implicit referenced derived fields for the sub-model except ones defined in the mining schema.

Referenced derived fields.

All input fields in an array.

All input names in an array.

The schema of inputs.

A series with all null values is returned when can not produce a result.

The number of class labels in a classification model.

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 class labels of all categorical targets.

The first target field for the supervised model.

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.

All target names in an array.

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.

User-defined custom output fields, both the internal output of PMML and predefined output are ignored when the field is specified.

The parent model.

A flag for whether to return those predefined output fields not exist in the output element explicitly.