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Sep 5, 2017 at 13:16 answer added Frank Harrell timeline score: 2
S Sep 5, 2017 at 12:36 history edited gung - Reinstate Monica CC BY-SA 3.0
Corrected spelling so question doesn't come up in searches for ROUGE
S Sep 5, 2017 at 12:36 history suggested Alan Buxton CC BY-SA 3.0
Corrected spelling so question doesn't come up in searches for ROUGE
Sep 5, 2017 at 12:23 review Suggested edits
S Sep 5, 2017 at 12:36
Mar 10, 2017 at 13:52 comment added Emil Filipov @kjetilbhalvorsen Meaning. I don't know whether or not i will get the truth about my predictions. The client may go bad in a few months and that will show that i was kind of right about him, but he may stay a paying customer, although with some delay, for the rest of his existence. I guess another way to go about it is to just take really high risk customers and wait for the truth about them?
Mar 10, 2017 at 13:47 comment added Emil Filipov @kjetilbhalvorsen Well, let me tell you a bit more about the data. I have customers which are with status A and are fine and dandy. I have customers that have a status B, which means they are canceled because they stopped paying their bills (in other words - bad customers). I have a few, around 20, non-static measurements that will 99% surely change with time. This is why i take only the probability and if it is lets say more then 50% to go bad i tell other departments to contact these customers to work the situation out so they don't get status B.
Mar 10, 2017 at 13:42 comment added kjetil b halvorsen If you use this online via some computer system, then you will get predictions from your model. Stote them, and when the "truth" arives for the case, store it too.So can can compute some cumulative measure of misfit, and when that seems to jump, there might be time to reconsider your model.
Mar 10, 2017 at 13:32 history asked Emil Filipov CC BY-SA 3.0