Package org.apache.spark.ml.attribute

ML attributes

See: Description

Interface Summary

Interface	Description
AttributeFactory	Trait for ML attribute factories.

Class Summary

Class	Description
Attribute	Abstract class for ML attributes.
AttributeGroup	Attributes that describe a vector ML column.
AttributeKeys	Keys used to store attributes.
AttributeType	An enum-like type for attribute types: AttributeType$.Numeric, AttributeType$.Nominal, and AttributeType$.Binary.
BinaryAttribute	A binary attribute.
NominalAttribute	A nominal attribute.
NumericAttribute	A numeric attribute with optional summary statistics.
UnresolvedAttribute	An unresolved attribute.

Package org.apache.spark.ml.attribute Description

ML attributes

The ML pipeline API uses Datasets as ML datasets. Each dataset consists of typed columns, e.g., string, double, vector, etc. However, knowing only the column type may not be sufficient to handle the data properly. For instance, a double column with values 0.0, 1.0, 2.0, ... may represent some label indices, which cannot be treated as numeric values in ML algorithms, and, for another instance, we may want to know the names and types of features stored in a vector column. ML attributes are used to provide additional information to describe columns in a dataset.

ML columns

A column with ML attributes attached is called an ML column. The data in ML columns are stored as double values, i.e., an ML column is either a scalar column of double values or a vector column. Columns of other types must be encoded into ML columns using transformers. We use Attribute to describe a scalar ML column, and AttributeGroup to describe a vector ML column. ML attributes are stored in the metadata field of the column schema.