Timeline for Data preparation and machine learning algorithm for click prediction
Current License: CC BY-SA 3.0
12 events
| when toggle format | what | by | license | comment | |
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| Jul 10, 2014 at 11:03 | comment | added | aasthetic | for selecting what users will click on, we will have to search our system for keywords etc as relevant. so if there is a way to reject a request without delaying time in keyword lookup, it will save a lot of time in processing for us | |
| S Jul 9, 2014 at 0:19 | history | suggested | Air | CC BY-SA 3.0 |
proofreading grammar
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| Jul 3, 2014 at 3:49 | comment | added | cwharland | The class imbalance is such that you don't really want to predict click vs no click. Since almost no one clicks your best result is to simply predict they don't click. Instead, think about predicting for all the users that click...which ad will they click on. This is a much more useful problem. | |
| Jul 2, 2014 at 15:52 | answer | added | Simon | timeline score: 2 | |
| Jun 30, 2014 at 17:44 | comment | added | aasthetic | That is what I meant by how the model does not look good. I think this is due to the sparsity of instances of clicks happening and random nature of event of clicks happening. So how can I go about sampling the data? Also how should I add weights to the parameters for improving the prediction? Sorry for adding more open-ended questions but encountering issues as they come, I also want to ask how to work with large datasets in R? | |
| Jun 30, 2014 at 17:13 | comment | added | Air | Thanks. There's an "edit" link at the bottom of your question so that you can update it with this additional information. | |
| Jun 30, 2014 at 17:02 | comment | added | aasthetic | Sorry,an eg: On trying logit in R with these values: day-28, hour-11,day of the week-7, state-New Mexico, OS-iOS, OS Version-7.0, browser family-Mobile Safari, browser version-7.0, device manufacturer-apple, IP carrier- Comcast, user age-20, gender-male, click happened-yes, predicted probability from GLM in R-0.000000001. In another instance,day-28, hour-12th, day of the week-7, state-Connecticut, OS-iOS, OS version-7.0, browser-mobile safari, browser version-7.0, device manufacturer-apple, IP carrier-Comcast, user age-22, user gender-female,click happened-no, predicted probability-.046 | |
| Jun 30, 2014 at 16:23 | review | Close votes | |||
| Jul 17, 2014 at 3:08 | |||||
| Jun 30, 2014 at 15:52 | comment | added | Air | That is a lot of open ended questions to ask all at once. One of the goals of this site is that questions and their answers should be useful to future visitors. Can you be more specific? What looks bad in your linear model? When you tried the logistic model, how did it come out? Were there specific problems? | |
| Jun 30, 2014 at 15:49 | review | Suggested edits | |||
| S Jul 9, 2014 at 0:19 | |||||
| Jun 30, 2014 at 12:25 | review | First posts | |||
| Jul 2, 2014 at 12:12 | |||||
| Jun 30, 2014 at 12:05 | history | asked | aasthetic | CC BY-SA 3.0 |