Hi all, Recently I played a little bit with both naive and mllib python sample codes for logistic regression. In short I wanted to compare naive and non naive logistic regression results using same input weights and data. So, I modified slightly both sample codes to use the same initial weights and generated a text file containing lines of label and features separated by spaces.
After one iteration the computed weights are the same (nice !), but on the second iteration the computed weights are different (and obviously for the remaining iterations too) Maybe this behaviour is related to the default regularizer and regularization parameter used by the mllib implementation of LogisticRegressionWithSGD ? What is the difference between the naive implementation and the mllib implementation of logisticRegression with stochastic gradient descent ? Thanks Cedric -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/About-logistic-regression-sample-codes-in-pyspark-tp21015.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
