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update doc
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python/docs/pyspark.ml.rst

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----------
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pyspark.ml module
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-------------------------
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-----------------
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.. automodule:: pyspark.ml
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:members:
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:undoc-members:
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:show-inheritance:
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pyspark.ml.param module
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-----------------------
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.. automodule:: pyspark.ml.param
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:members:
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:undoc-members:
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:show-inheritance:
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pyspark.ml.feature module
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-------------------------
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.. automodule:: pyspark.ml.feature
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:members:
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:undoc-members:
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:show-inheritance:
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pyspark.ml.classification module
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--------------------------------
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.. automodule:: pyspark.ml.classification
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:members:
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:undoc-members:
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:show-inheritance:

python/pyspark/ml/__init__.py

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from pyspark.ml.param import Param, Params
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from pyspark.ml.util import Identifiable
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__all__ = ["Pipeline", "Transformer", "Estimator"]
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__all__ = ["Pipeline", "Transformer", "Estimator", "param", "feature", "classification"]
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def _jvm():

python/pyspark/ml/classification.py

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# limitations under the License.
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#
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from pyspark.sql import SchemaRDD
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from pyspark.sql import SchemaRDD, inherit_doc
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from pyspark.ml import Estimator, Transformer, _jvm
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from pyspark.ml.param import Param
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@inherit_doc
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class LogisticRegression(Estimator):
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"""
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Logistic regression.

python/pyspark/ml/feature.py

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# limitations under the License.
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#
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from pyspark.sql import SchemaRDD, ArrayType, StringType
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from pyspark.sql import SchemaRDD, ArrayType, StringType, inherit_doc
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from pyspark.ml import Transformer, _jvm
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from pyspark.ml.param import Param
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@inherit_doc
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class Tokenizer(Transformer):
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def __init__(self):
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raise ValueError("The input params must be either a dict or a list.")
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@inherit_doc
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class HashingTF(Transformer):
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def __init__(self):

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