starter code for click prediction project
question 1c of the warmup is complete
Rest of the implementation for the warmup exercise.
(1) Now correctly identifying data that is valid but missing.
(2) Now skipping user id zero in the unique user count.
(1) deleting extraneous files
(2) fixing some variable names
(3) making more concrete when we see -1 values for clicks (only in test data)
initial implementation, unsure if formulas are correct
now computing predictions for test data
Now reporting averaged weights
Now computing a running average instead of potentially overflowing the accumulator.  This is big data after all.
parts 1.4.2 (b) and (c) are done
plotting average loss
fixing the format of the average loss output files
part 1.4.3
moved delayed regularization into if prediction != outcome block
finalized tests for 1.4.3
1.4.4 hashed features and extra credit personalized models
re-confirmed that all tests pass (except for the 1million hashed one, that takes too long)
used weights from linear regression plugged into the sigmoid to see how the labels change
added RMSE computation for personalized prediction done only on users residing in both the training and test datasets
regularization needs to happen even when gender and age are missing