diff --git a/python/docs/pyspark.ml.rst b/python/docs/pyspark.ml.rst
index f20453e624296..f4c1a6406e020 100644
--- a/python/docs/pyspark.ml.rst
+++ b/python/docs/pyspark.ml.rst
@@ -34,4 +34,5 @@ pyspark.ml.classification module
 .. automodule:: pyspark.ml.classification
     :members:
     :undoc-members:
+    :inherited-members:
     :show-inheritance:
diff --git a/python/pyspark/ml/__init__.py b/python/pyspark/ml/__init__.py
index 83351dc523ce1..4666ce7bc2499 100644
--- a/python/pyspark/ml/__init__.py
+++ b/python/pyspark/ml/__init__.py
@@ -29,6 +29,20 @@ def _jvm():
     return SparkContext._jvm
 
 
+def _inherit_doc(cls):
+    for name, func in vars(cls).items():
+        # only inherit docstring for public functions
+        if name.startswith("_"):
+            continue
+        if not func.__doc__:
+            for parent in cls.__bases__:
+                parent_func = getattr(parent, name, None)
+                if parent_func and getattr(parent_func, "__doc__", None):
+                    func.__doc__ = parent_func.__doc__
+                    break
+    return cls
+
+
 @inherit_doc
 class PipelineStage(Params):
     """
diff --git a/python/pyspark/ml/classification.py b/python/pyspark/ml/classification.py
index f3cea0958c897..fd1fb906ca5c1 100644
--- a/python/pyspark/ml/classification.py
+++ b/python/pyspark/ml/classification.py
@@ -17,11 +17,13 @@
 
 from pyspark.sql import SchemaRDD, inherit_doc
 from pyspark.ml import Estimator, Transformer, _jvm
-from pyspark.ml.param import Param
+from pyspark.ml.param.shared import HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,\
+    HasRegParam
 
 
 @inherit_doc
-class LogisticRegression(Estimator):
+class LogisticRegression(Estimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter,
+                         HasRegParam):
     """
     Logistic regression.
     """
@@ -29,31 +31,8 @@ class LogisticRegression(Estimator):
     # _java_class = "org.apache.spark.ml.classification.LogisticRegression"
 
     def __init__(self):
+        super(LogisticRegression, self).__init__()
         self._java_obj = _jvm().org.apache.spark.ml.classification.LogisticRegression()
-        self.maxIter = Param(self, "maxIter", "max number of iterations", 100)
-        self.regParam = Param(self, "regParam", "regularization constant", 0.1)
-        self.featuresCol = Param(self, "featuresCol", "features column name", "features")
-
-    def setMaxIter(self, value):
-        self._java_obj.setMaxIter(value)
-        return self
-
-    def getMaxIter(self):
-        return self._java_obj.getMaxIter()
-
-    def setRegParam(self, value):
-        self._java_obj.setRegParam(value)
-        return self
-
-    def getRegParam(self):
-        return self._java_obj.getRegParam()
-
-    def setFeaturesCol(self, value):
-        self._java_obj.setFeaturesCol(value)
-        return self
-
-    def getFeaturesCol(self):
-        return self._java_obj.getFeaturesCol()
 
     def fit(self, dataset, params=None):
         """
diff --git a/python/pyspark/ml/param.py b/python/pyspark/ml/param/__init__.py
similarity index 78%
rename from python/pyspark/ml/param.py
rename to python/pyspark/ml/param/__init__.py
index ffe58a6ee69d7..89e5d732f7586 100644
--- a/python/pyspark/ml/param.py
+++ b/python/pyspark/ml/param/__init__.py
@@ -19,7 +19,6 @@
 
 from pyspark.ml.util import Identifiable
 
-
 __all__ = ["Param"]
 
 
@@ -29,16 +28,18 @@ class Param(object):
     """
 
     def __init__(self, parent, name, doc, defaultValue=None):
+        if not isinstance(parent, Identifiable):
+            raise ValueError("Parent must be identifiable but got type %s." % type(parent).__name__)
         self.parent = parent
-        self.name = name
-        self.doc = doc
+        self.name = str(name)
+        self.doc = str(doc)
         self.defaultValue = defaultValue
 
     def __str__(self):
-        return self.parent + "_" + self.name
+        return str(self.parent) + "_" + self.name
 
     def __repr__(self):
-        return self.parent + "_" + self.name
+        return str(self.parent) + "_" + self.name
 
 
 class Params(Identifiable):
@@ -49,10 +50,11 @@ class Params(Identifiable):
 
     __metaclass__ = ABCMeta
 
+    #: Internal param map.
+    paramMap = {}
+
     def __init__(self):
         super(Params, self).__init__()
-        #: Internal param map.
-        self.paramMap = {}
 
     def params(self):
         """
@@ -60,4 +62,4 @@ def params(self):
         :py:func:`dir` to get all attributes of type
         :py:class:`Param`.
         """
-        return [attr for attr in dir(self) if isinstance(attr, Param)]
+        return filter(lambda x: isinstance(x, Param), map(lambda x: getattr(self, x), dir(self)))
diff --git a/python/pyspark/ml/param/_gen_shared_params.py b/python/pyspark/ml/param/_gen_shared_params.py
new file mode 100644
index 0000000000000..8c3aa7eba9483
--- /dev/null
+++ b/python/pyspark/ml/param/_gen_shared_params.py
@@ -0,0 +1,80 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+header = """#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#"""
+
+
+def _gen_param_code(name, doc, defaultValue):
+    """
+    Generates Python code for a shared param class.
+
+    :param name: param name
+    :param doc: param doc
+    :param defaultValue: string representation of the param
+    :return: code string
+    """
+    upperCamelName = name[0].upper() + name[1:]
+    return """class Has%s(Params):
+
+    def __init__(self):
+        super(Has%s, self).__init__()
+        #: %s
+        self.%s = Param(self, "%s", "%s", %s)
+
+    def set%s(self, value):
+        self.paramMap[self.%s] = value
+        return self
+
+    def get%s(self, value):
+        if self.%s in self.paramMap:
+            return self.paramMap[self.%s]
+        else:
+            return self.defaultValue""" % (
+        upperCamelName, upperCamelName, doc, name, name, doc, defaultValue, upperCamelName, name,
+        upperCamelName, name, name)
+
+if __name__ == "__main__":
+    print header
+    print "\n# DO NOT MODIFY. The code is generated by _gen_shared_params.py.\n"
+    print "from pyspark.ml.param import Param, Params\n\n"
+    shared = [
+        ("maxIter", "max number of iterations", "100"),
+        ("regParam", "regularization constant", "0.1"),
+        ("featuresCol", "features column name", "'features'"),
+        ("labelCol", "label column name", "'label'"),
+        ("predictionCol", "prediction column name", "'prediction'"),
+        ("inputCol", "input column name", "'input'"),
+        ("outputCol", "output column name", "'output'")]
+    code = []
+    for name, doc, defaultValue in shared:
+        code.append(_gen_param_code(name, doc, defaultValue))
+    print "\n\n\n".join(code)
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py
new file mode 100644
index 0000000000000..88afb5481f7b8
--- /dev/null
+++ b/python/pyspark/ml/param/shared.py
@@ -0,0 +1,146 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+# DO NOT MODIFY. The code is generated by _gen_shared_params.py.
+
+from pyspark.ml.param import Param, Params
+
+
+class HasMaxIter(Params):
+
+    def __init__(self):
+        super(HasMaxIter, self).__init__()
+        #: max number of iterations
+        self.maxIter = Param(self, "maxIter", "max number of iterations", 100)
+
+    def setMaxIter(self, value):
+        self.paramMap[self.maxIter] = value
+        return self
+
+    def getMaxIter(self, value):
+        if self.maxIter in self.paramMap:
+            return self.paramMap[self.maxIter]
+        else:
+            return self.defaultValue
+
+
+class HasRegParam(Params):
+
+    def __init__(self):
+        super(HasRegParam, self).__init__()
+        #: regularization constant
+        self.regParam = Param(self, "regParam", "regularization constant", 0.1)
+
+    def setRegParam(self, value):
+        self.paramMap[self.regParam] = value
+        return self
+
+    def getRegParam(self, value):
+        if self.regParam in self.paramMap:
+            return self.paramMap[self.regParam]
+        else:
+            return self.defaultValue
+
+
+class HasFeaturesCol(Params):
+
+    def __init__(self):
+        super(HasFeaturesCol, self).__init__()
+        #: features column name
+        self.featuresCol = Param(self, "featuresCol", "features column name", 'features')
+
+    def setFeaturesCol(self, value):
+        self.paramMap[self.featuresCol] = value
+        return self
+
+    def getFeaturesCol(self, value):
+        if self.featuresCol in self.paramMap:
+            return self.paramMap[self.featuresCol]
+        else:
+            return self.defaultValue
+
+
+class HasLabelCol(Params):
+
+    def __init__(self):
+        super(HasLabelCol, self).__init__()
+        #: label column name
+        self.labelCol = Param(self, "labelCol", "label column name", 'label')
+
+    def setLabelCol(self, value):
+        self.paramMap[self.labelCol] = value
+        return self
+
+    def getLabelCol(self, value):
+        if self.labelCol in self.paramMap:
+            return self.paramMap[self.labelCol]
+        else:
+            return self.defaultValue
+
+
+class HasPredictionCol(Params):
+
+    def __init__(self):
+        super(HasPredictionCol, self).__init__()
+        #: prediction column name
+        self.predictionCol = Param(self, "predictionCol", "prediction column name", 'prediction')
+
+    def setPredictionCol(self, value):
+        self.paramMap[self.predictionCol] = value
+        return self
+
+    def getPredictionCol(self, value):
+        if self.predictionCol in self.paramMap:
+            return self.paramMap[self.predictionCol]
+        else:
+            return self.defaultValue
+
+
+class HasInputCol(Params):
+
+    def __init__(self):
+        super(HasInputCol, self).__init__()
+        #: input column name
+        self.inputCol = Param(self, "inputCol", "input column name", 'input')
+
+    def setInputCol(self, value):
+        self.paramMap[self.inputCol] = value
+        return self
+
+    def getInputCol(self, value):
+        if self.inputCol in self.paramMap:
+            return self.paramMap[self.inputCol]
+        else:
+            return self.defaultValue
+
+
+class HasOutputCol(Params):
+
+    def __init__(self):
+        super(HasOutputCol, self).__init__()
+        #: output column name
+        self.outputCol = Param(self, "outputCol", "output column name", 'output')
+
+    def setOutputCol(self, value):
+        self.paramMap[self.outputCol] = value
+        return self
+
+    def getOutputCol(self, value):
+        if self.outputCol in self.paramMap:
+            return self.paramMap[self.outputCol]
+        else:
+            return self.defaultValue
diff --git a/python/pyspark/ml/util.py b/python/pyspark/ml/util.py
index 801a5eeaa3249..5d74088b0b13e 100644
--- a/python/pyspark/ml/util.py
+++ b/python/pyspark/ml/util.py
@@ -27,3 +27,9 @@ def __init__(self):
         #: A unique id for the object. The default implementation
         #: concatenates the class name, "-", and 8 random hex chars.
         self.uid = type(self).__name__ + "-" + uuid.uuid4().hex[:8]
+
+    def __str__(self):
+        return self.uid
+
+    def __repr__(self):
+        return str(self)