Github user yanboliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/4831#discussion_r25582380
--- Diff: python/pyspark/mllib/classification.py ---
@@ -207,7 +207,7 @@ def train(cls, data, iterations=100,
initialWeights=None, regParam=0.01, regType
"""
def train(rdd, i):
return callMLlibFunc("trainLogisticRegressionModelWithLBFGS",
rdd, int(iterations), i,
- float(regParam), str(regType),
bool(intercept), int(corrections),
+ float(regParam), regType,
bool(intercept), int(corrections),
--- End diff --
I think directly use regType id enough, because py4j can translate
"l1","l2",None in Python to "l1","l2",null in Java/Scala.
And I found the peer functions LogisticRegressionWithSGD and SVMWithSGD
also directly use regType and it can work well.
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