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<h1 title="Package" class="title">Package org.apache.spark.ml.attribute</h1>
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<div class="block">ML attributes</div>
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<p>See: <a href="#package_description">Description</a></p>
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<th class="colFirst" scope="col">Class</th>
<th class="colLast" scope="col">Description</th>
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<td class="colFirst"><a href="../../../../../org/apache/spark/ml/attribute/Attribute.html" title="class in org.apache.spark.ml.attribute">Attribute</a></td>
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<div class="block">:: DeveloperApi ::
Abstract class for ML attributes.</div>
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<td class="colFirst"><a href="../../../../../org/apache/spark/ml/attribute/AttributeGroup.html" title="class in org.apache.spark.ml.attribute">AttributeGroup</a></td>
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<div class="block">:: DeveloperApi ::
Attributes that describe a vector ML column.</div>
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<td class="colFirst"><a href="../../../../../org/apache/spark/ml/attribute/AttributeType.html" title="class in org.apache.spark.ml.attribute">AttributeType</a></td>
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<div class="block">:: DeveloperApi ::
An enum-like type for attribute types: <code>AttributeType$.Numeric</code>, <code>AttributeType$.Nominal</code>,
and <code>AttributeType$.Binary</code>.</div>
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<td class="colFirst"><a href="../../../../../org/apache/spark/ml/attribute/BinaryAttribute.html" title="class in org.apache.spark.ml.attribute">BinaryAttribute</a></td>
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<div class="block">:: DeveloperApi ::
A binary attribute.</div>
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<td class="colFirst"><a href="../../../../../org/apache/spark/ml/attribute/NominalAttribute.html" title="class in org.apache.spark.ml.attribute">NominalAttribute</a></td>
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<div class="block">:: DeveloperApi ::
A nominal attribute.</div>
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<td class="colFirst"><a href="../../../../../org/apache/spark/ml/attribute/NumericAttribute.html" title="class in org.apache.spark.ml.attribute">NumericAttribute</a></td>
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A numeric attribute with optional summary statistics.</div>
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An unresolved attribute.</div>
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<h2 title="Package org.apache.spark.ml.attribute Description">Package org.apache.spark.ml.attribute Description</h2>
<div class="block"><h2>ML attributes</h2>
The ML pipeline API uses <a href="../../../../../org/apache/spark/sql/DataFrame.html" title="class in org.apache.spark.sql"><code>DataFrame</code></a>s 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.
<h3>ML columns</h3>
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 <a href="../../../../../org/apache/spark/ml/attribute/Attribute.html" title="class in org.apache.spark.ml.attribute"><code>Attribute</code></a> to describe a scalar ML column, and
<a href="../../../../../org/apache/spark/ml/attribute/AttributeGroup.html" title="class in org.apache.spark.ml.attribute"><code>AttributeGroup</code></a> to describe a vector ML column.
ML attributes are stored in the metadata field of the column schema.</div>
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