[SPARK-13676] Fix mismatched default values for regParam in LogisticR…

…egression

## What changes were proposed in this pull request?

The default value of regularization parameter for `LogisticRegression` algorithm is different in Scala and Python. We should provide the same value.

**Scala**
```
scala> new org.apache.spark.ml.classification.LogisticRegression().getRegParam
res0: Double = 0.0
```

**Python**
```
>>> from pyspark.ml.classification import LogisticRegression
>>> LogisticRegression().getRegParam()
0.1
```

## How was this patch tested?
manual. Check the following in `pyspark`.
```
>>> from pyspark.ml.classification import LogisticRegression
>>> LogisticRegression().getRegParam()
0.0
```

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11519 from dongjoon-hyun/SPARK-13676.

python/pyspark/ml/classification.py

@@ -79,33 +79,33 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredicti
 
     @keyword_only
     def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                 maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
+                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
                  threshold=0.5, thresholds=None, probabilityCol="probability",
                  rawPredictionCol="rawPrediction", standardization=True, weightCol=None):
         """
         __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                 maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
+                 maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
                  threshold=0.5, thresholds=None, probabilityCol="probability", \
                  rawPredictionCol="rawPrediction", standardization=True, weightCol=None)
         If the threshold and thresholds Params are both set, they must be equivalent.
         """
         super(LogisticRegression, self).__init__()
         self._java_obj = self._new_java_obj(
             "org.apache.spark.ml.classification.LogisticRegression", self.uid)
-        self._setDefault(maxIter=100, regParam=0.1, tol=1E-6, threshold=0.5)
+        self._setDefault(maxIter=100, regParam=0.0, tol=1E-6, threshold=0.5)
         kwargs = self.__init__._input_kwargs
         self.setParams(**kwargs)
         self._checkThresholdConsistency()
 
     @keyword_only
     @since("1.3.0")
     def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction",
-                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
+                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True,
                   threshold=0.5, thresholds=None, probabilityCol="probability",
                   rawPredictionCol="rawPrediction", standardization=True, weightCol=None):
         """
         setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \
-                  maxIter=100, regParam=0.1, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
+                  maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \
                   threshold=0.5, thresholds=None, probabilityCol="probability", \
                   rawPredictionCol="rawPrediction", standardization=True, weightCol=None)
         Sets params for logistic regression.