Hi All, I am trying to perform regularized logistic regression with mllib in python. I have seen that this is possible in the following scala example: https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/mllib/BinaryClassification.scala
But I do not see any way to set the regType and regParam when training logistic regression through python. Additionally, I would like to output the activations -- i.e. P(Y=1 | X). Currently, LogisticRegressionModel.predict() just thresholds at 0.5 and does not return the actual probability. Do I just have to do this by hand by grabbing the weights from the trained model, or is there a built in way to do this? Best, Francisco Gimenez -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/MLLib-Regularized-logistic-regression-in-python-tp9780.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
