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.

Common attributes of this model

An informational string describing the technique used by the model designer to establish the baseline scores.

A single value to use as the baseline comparison score for all characteristics, when determining reason code ranking. Alternatively, unique baseline scores may be set for each individual Characteristic as shown below. This attribute is required only when useReasonCodes is "true" and attribute baselineScore is not given for each Characteristic.

The schema of candidate outputs.

Returns class labels of the specified target.

The class labels in a classification model.

Creates an object of subclass of ModelOutputs 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.

Returns PMML version as a double value

The data dictionary of this model.

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

Encodes the input series.

Returns the field of a given name.

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.

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

The header of this model.

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

Returns importances of predictors.

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

Initial score contains a value which is added to the overall score whenever partial scores are summed up.

Referenced derived fields.

All input fields in an array.

All input names in an array.

The schema of inputs.

Tests if this is a association rules model.

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.

Tests if this is a clustering model.

Tests if this is a mixed model.

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.

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 time series model.

The optional local transformations.

Model element type.

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.

Returns the number of class labels of the specified target.

The number of class labels in a classification model.

Returns optype of the specified target.

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 parent model.

The scoring procedure for a scorecard is simple. Partial scores are summed up to create an overall score, the result of the scorecard.

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]}

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 data map of Java.

Predicts values for a given data map.

Pre-process the input series.

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

May be "pointsAbove" or "pointsBelow", describing how reason codes shall be ranked, relative to the baseline score of each Characteristic, or as set at the top-level scorecard.

Predicts value for a given data series

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 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.

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.

All target names in an array.

The optional transformation dictionary.

If "false", reason codes are not computed as part of the scorecard.

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.

PMML version.