Sequence learning using Conditional Random Fields?

Question

I am new to sequential learning (& machine learning) & am trying to understand how to use conditional random fields to solve my problem.

I have a dataset which i

Answer 1

1. Yes, since you are trying to predict two label ( Facility and Day ), concatenating of labels will be required. Else, you can also learn 2 different models for predicting each label (see point 3).

2. I think you should look into regression models for this problem rather than CRFs.

3. I think the arrangement of the data should be in such a way that log history of a user is learned easily. Can you tell me the 'minimum' history you have for 'any' user ( last 3 logins? 5 logins? 7 logins? ) ?

Assuming you have last 3 logins of every user. Then, if in your place, I would arrange the data in a different manner and learn 2 different models, one to predict day and another to predict facility. An example of arrangement of data and template file for predicting day is here. You similarly, change name of days of week to facility names and learn a model for predicting facility. Also you can think of and add more features to the ones that I have suggested. If you have more user data (say occupation or age or something ) then you should definitely try adding more columns to the training data and add these columns as features in template file. Remember, the testing file should arranged in the same way as training file (except last column can be empty/missing, because it is the label that is to be predicted by the model during testing).

If you want to go ahead and predict both label in one model, you can try concatenation (in the example that I've given you, day will now become day_facility).