I am testing the LogisticRegression performance on a synthetically generated
data. The weights I have as input are
w = [2, 3, 4]
with no intercept and three features. After training on 1000 synthetically
generated datapoint assuming random normal distribution for each, the Spark
LogisticRegression model I obtain has weights as
[6.005520656096823,9.35980263762698,12.203400879214152]
I can see that each weight is scaled by a factor close to '3' w.r.t. the
original values. I am unable to guess the reason behind this. The code is
simple enough as
/*
* Logistic Regression model
*/
val lr = new LogisticRegression()
.setMaxIter(50)
.setRegParam(0.001)
.setElasticNetParam(0.95)
.setFitIntercept(false)
val lrModel = lr.fit(trainingData)
println(s"${lrModel.weights}")
I would greatly appreciate if someone could shed some light on what's fishy
here.
with kind regards, Nikhil
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