Probabilistic classifier useful for both numeric and nominal features

First, I should say I'm beginner in data mining and use Matlab. I have a huge dataset: approximately 40 millions observation. It consists of 24 features including numeric and nominal values like below:

id, click, hour, C1, banner_pos, site_id, site_domain, site_category, app_id, app_domain, app_category, device_id, device_ip, device_model, device_typ,e device_conn_type, C14


one observation is:

1.00E+18,   0,  14102100,   1005,   0,  1fbe01fe,   f3845767,   28905ebd,   ecad2386,   7801e8d9,   07d7df22,   a99f214a,   ddd2926e,   44956a24,   1,  2,  15706


All the features and some headers are annonymized.

The problem is to estimate the probability of clicking an advertisement id (click is target feature). What I need is a probabilistic classifier to estimate this that also uses both nominal and numeric features. Lately I used Naive Bayes but it just use numeric and does not give accurate predictions. What is your suggestion?

• Please consider editing your question because we could only guess what does "device_conn_type" or "C4" etc. mean. – Tim Dec 12 '14 at 18:29
• I edited your question so that it will be more readable. Next time consider paying more attention to text formatting. Btw, welcome to CV! Consider taking a tour that will help you to know the site better and get the best of it. – Tim Dec 12 '14 at 18:47
• I appreciate you Tim to helping me not to holding on this question. – Milad Dec 13 '14 at 7:15