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Type Members
- abstract class AbstractField extends Field
Abstract class for field in a PMML with common implementations.
- trait Attribute extends HasLabels with HasMissingValues with HasInvalidValues with HasValidValues with HasIntervals with ValueIndexer with Serializable
- sealed trait AttributeType extends AnyRef
- abstract class CategoricalAttribute extends Attribute
- class ContinuousAttribute extends Attribute with HasIntervals
- class DataDictionary extends Dictionary[DataField] with PmmlElement
Contains definitions for fields as used in mining models.
Contains definitions for fields as used in mining models. It specifies the types and value ranges. These definitions are assumed to be independent of specific data sets as used for training or scoring a specific model.
- class DataField extends AbstractField with PmmlElement
Defines a field as used in mining models.
Defines a field as used in mining models. It specifies the types and value ranges.
- class Decision extends PmmlElement
- class Decisions extends PmmlElement
The Decisions element contains an element Decision for every possible value of the decision.
- abstract class Dictionary[T <: Field] extends Seq[T] with HasField
- abstract class Field extends HasDataType with HasOpType with Attribute
Abstract class for field in a PMML.
- trait FieldScope extends HasField
- sealed trait FieldType extends AnyRef
- trait HasField extends AnyRef
- trait HasFieldScope extends AnyRef
- trait HasInvalidValues extends AnyRef
- trait HasLabels extends AnyRef
- trait HasMiningSchema extends AnyRef
- trait HasMissingValues extends AnyRef
- trait HasOutput extends AnyRef
- trait HasOutputFields extends AnyRef
The Output section in the model specifies names for columns in an output table and describes how to compute the corresponding values.
- trait HasTargetFields extends AnyRef
- trait HasTargets extends AnyRef
- trait HasUsageType extends AnyRef
- trait HasValidValues extends AnyRef
- class ImmutableCategoricalAttribute extends CategoricalAttribute
- class MiningField extends HasUsageType with PmmlElement
MiningFields also define the usage of each field (active, supplementary, target, ...) as well as policies for treating missing, invalid or outlier values.
- class MiningSchema extends HasTargetFields with PmmlElement
The MiningSchema is the Gate Keeper for its model element.
The MiningSchema is the Gate Keeper for its model element. All data entering a model must pass through the MiningSchema. Each model element contains one MiningSchema which lists fields as used in that model. While the MiningSchema contains information that is specific to a certain model, the DataDictionary contains data definitions which do not vary per model. The main purpose of the MiningSchema is to list the fields that have to be provided in order to apply the model.
- trait MutableAttribute extends Attribute with MutableValueIndexer
- class MutableCategoricalAttribute extends CategoricalAttribute
- class MutableFieldScope[T <: Field] extends FieldScope
- trait MutableValueIndexer extends ValueIndexer
- class Output extends HasOutputFields with HasField with PmmlElement
Output element describes a set of result values that can be returned from a model.
- class OutputField extends AbstractField with PmmlElement
OutputField elements specify names, types and rules for calculating specific result features.
OutputField elements specify names, types and rules for calculating specific result features. This information can be used while writing an output table.
- class Target extends PmmlElement
Note that castInteger, min, max, rescaleConstant and rescaleFactor only apply to models of type regression.
Note that castInteger, min, max, rescaleConstant and rescaleFactor only apply to models of type regression. Furthermore, they must be applied in sequence, which is:
min and max rescaleFactor rescaleConstant castInteger
- class TargetValue extends PmmlElement
- class Targets extends HasTargetFields with PmmlElement
- trait ValueIndexer extends AnyRef
- class WrappedField extends Field
Defines the wrapped field that contains an internal field acts all operations.
Value Members
- object Algorithm extends Enumeration
Specifies which scoring algorithm to use when computing the output value.
Specifies which scoring algorithm to use when computing the output value. It applies only to Association Rules models.
- object Attribute extends Serializable
- object AttributeType
- object CastInteger extends Enumeration
If a regression model should predict integers, use the attribute castInteger to control how decimal places should be handled.
- object CategoricalAttribute extends Serializable
- object ContinuousAttribute extends Serializable
- object DataDictionary extends Serializable
- object FieldType
- object InvalidValueTreatment extends Enumeration
This field specifies how invalid input values are handled.
This field specifies how invalid input values are handled.
- returnInvalid is the default and specifies that, when an invalid input is encountered, the model should return a value indicating an invalid result has been returned.
- asIs means to use the input without modification.
- asMissing specifies that an invalid input value should be treated as a missing value and follow the behavior specified by the missingValueReplacement attribute if present (see above). If asMissing is specified but there is no respective missingValueReplacement present, a missing value is passed on for eventual handling by successive transformations via DerivedFields or in the actual mining model. - asValue specifies that an invalid input value should be replaced with the value specified by attribute invalidValueReplacement which must be present in this case, or the PMML is invalid.
- object MissingValueTreatment extends Enumeration
In a PMML consumer this field is for information only, unless the value is returnInvalid, in which case if a missing value is encountered in the given field, the model should return a value indicating an invalid result; otherwise, the consumer only looks at missingValueReplacement - if a value is present it replaces missing values.
In a PMML consumer this field is for information only, unless the value is returnInvalid, in which case if a missing value is encountered in the given field, the model should return a value indicating an invalid result; otherwise, the consumer only looks at missingValueReplacement - if a value is present it replaces missing values. Except as described above, the missingValueTreatment attribute just indicates how the missingValueReplacement was derived, but places no behavioral requirement on the consumer.
- object OutlierTreatmentMethod extends Enumeration
Outliers
Outliers
- asIs: field values treated at face value.
- asMissingValues: outlier values are treated as if they were missing.
- asExtremeValues: outlier values are changed to a specific high or low value defined in MiningField.
- object OutputField extends Serializable
- object RankBasis extends Enumeration
Applies only to Association Rules and is used to specify which criterion is used to sort the output result.
Applies only to Association Rules and is used to specify which criterion is used to sort the output result. For instance, the result could be sorted by the confidence, support or lift of the rules.
- object RankOrder extends Enumeration
Determines the sorting order when ranking the results.
Determines the sorting order when ranking the results. The default behavior (rankOrder="descending") indicates that the result with the highest rank will appear first on the sorted list.
- object ResultFeature extends Enumeration
Result Features
- object RuleFeature extends Enumeration
Specifies which feature of an association rule to return.
Specifies which feature of an association rule to return. This attribute has been deprecated as of PMML 4.2. The rule feature values can now be specified in the feature attribute.
- Annotations
- @PmmlDeprecated("4.2")
- object TypelessAttribute extends Attribute
- object UsageType extends Enumeration
Usage type
Usage type
- active: field used as input (independent field).
- target: field that was used a training target for supervised models.
- predicted: field whose value is predicted by the model. As of PMML 4.2, this is deprecated and it has been replaced by the usage type target.
- supplementary: field holding additional descriptive information. Supplementary fields are not required to apply a model. They are provided as additional information for explanatory purpose, though. When some field has gone through preprocessing transformations before a model is built, then an additional supplementary field is typically used to describe the statistics for the original field values.
- group: field similar to the SQL GROUP BY. For example, this is used by AssociationModel and SequenceModel to group items into transactions by customerID or by transactionID.
- order: This field defines the order of items or transactions and is currently used in SequenceModel and TimeSeriesModel. Similarly to group, it is motivated by the SQL syntax, namely by the ORDER BY statement.
- frequencyWeight and analysisWeight: These fields are not needed for scoring, but provide very important information on how the model was built. Frequency weight usually has positive integer values and is sometimes called "replication weight". Its values can be interpreted as the number of times each record appears in the data. Analysis weight can have fractional positive values, it could be used for regression weight in regression models or for case weight in trees, etc. It can be interpreted as different importance of the cases in the model. Counts in ModelStats and Partitions can be computed using frequency weight, mean and standard deviation values can be computed using both weights.