I just used random numbers.
(My ML lib was spark-mllib_2.10-1.2.1)
Please see the attached log. In the middle of the log, I dumped the data set before feeding into LogisticRegressionWithLBFGS. The first column false/true was the label (attribute a), and columns 2-5 (attributes x, y, z, and i) were the features. The 6th column was just row ID and was not used.
The relationship was arbitrarily: a = (0.3 * x + 0.5 * y - 0.2 *z > 0.4)
After that you can find LBFGS was doing its job and then pumped out the error messages.
The model showed coefficients:
The last one was the intercept. As you can see, the model seemed close enough.
After that I fed the same data back to the model to see how the predictions worked. (here attribute a was the prediction and aa was the original label) I only displayed 20 rows.
The error rate showed 2 errors out of 1000.
count(INTEGER), errorRate(DOUBLE), countDiff(INTEGER)
1000, 0.0020000000949949026, 2
So, the algorithm worked, just spitting out the errors was kind of annoying. If this is not result affecting, maybe it should be warning or info.