.lambda(0.000001)
.stepOffset(10000)
.decayExponent(0.2);
for (int pass = 0; pass < 20; pass++) {
for (TelephoneCall observation : train) {
lr.train(observation.getTarget(), observation.asVector());
}
if (pass % 5 == 0) {
Auc eval = new Auc(0.5);
for (TelephoneCall testCall : test) {
eval.add(testCall.getTarget(), lr.classifyScalar(testCall.asVector()));