| Features | Descriptions |
|---|---|
| ip | Not Null |
| app | Not Null |
| device | Not Null |
| os | Not Null |
| channel | Not Null |
| click_time | Not Null |
| attributed_time | |
| is_attributed | 0 / 1 |
ip, app, device, os, channel
For each unique value of every basic feature, count the number of click that is attributed or not.
For each unique value of every basic feature, calculate the value's frequency in the whole dataset.
For each unique value of every basic feature, calculate the values conversion rate (i.e. the fraction, #is_attributed clicks / #clicks).
Select the features' combination whose features own a high value of correlation.
As for different time span(whole time, minute, and hour), calculate raw , average and standard deviation of all the features above.
So the amount of all above features is 3 * (20 + n) * 3.
Calculate every hour's conversion rate.
eg: temporal interval
| Num | Category | Features | AUC on dev set | AUC on test set | AUC in Real World |
|---|---|---|---|---|---|
| 1 | 1 | Basic #5 | 0.9774025893305988 | 0.9583 | |
| Basic #5 | 0.959279 | 0.963003 | 0.9559 | ||
| 2 | 1 | Basic #6 (hour) | 0.9765065318797589 | 0.9563 | |
| Basic #7(hour, day) | 0.960369 | 0.964231 | 0.9563 | ||
| 3 | "Basic" | 0.974860783943336 | 0.9684 | ||
| 4 | 1,2 | Add count | 0.9759619912016608 | ||
| 5 | 1,2 | Add attributed count | 0.9840863114093297 | 0.6239 | |
| 6 | 1,2 | Add attributed count (no hour's effect) | 0.9840863114093297 | 0.6114 | |
| 7 | 1,2 | Add count and attributed count | 0.9842108186300558 | ||
| 8 | 1,3 | Add Frequency | 0.9759619912016608 | ||
| 9 | 1,2,3 | Add count, attributed count, frequency | 0.9843591094736229 | ||
| 10 | 1,4 | Add count, conversion rate | |||
| 11 | 1,2,3,4 | Add count, attributed count, frequency, conversion | 0.9842607399131638 | ||
| 12 | 1,6 | Add hour count | 0.975100704348888 | ||
| 13 | 1,6 | Add hour attributed count auc | 0.9952688459421872 | 0.7051 | |
| 14 | 1,5 | Add ip_channel | 0.979456127558631 | 0.9622 | |
| 15 | 1,5 | Add app_channel | 0.9783578247039497 | 0.9592 | |
| 16 | 1,5 | Add ip_device | 0.9801894479874732 | 0.9628 | |
| 17 | 1,5 | Add All two degree of features | 0.9813457066441867 | 0.9646 | |
| 18 | 1,5 | Add All two degree of features(100,000,000) | 0.9656 | ||
| 19 | 1,5 | "Basic" + some features of high_importance | 0.9863726922286226 | 0.9684 |
PS: In Basic#5, click_time is transfered into hour and day.
Select device_ip, channel_app, app_ip, device, os_ip to add into Num3: "Basic" .
Normalized corr_rate = I(X;Y) / H(X,Y) = I(X;Y) / (H(X) + H(Y) - I(X;Y))
Mask = 0.1
app & channel: 0.269795263371
app & ip_channel: 0.104454990715
app & device_channel: 0.263802502515
app & os_channel: 0.174031886398
channel & ip_app: 0.105000757011
channel & app_device: 0.256371969166
channel & app_os: 0.148765039685
ip_app & device_channel: 0.109073593121
ip_app & os_channel: 0.150919655929
ip_channel & app_device: 0.105314833255
ip_channel & app_os: 0.136361783362
app_device & os_channel: 0.169265661127
app_os & device_channel: 0.149640128183
Mask = 0.2
app & channel: 0.269795263371
app & device_channel: 0.263802502515
channel & app_device: 0.256371969166
Mask = 0
ip & app: 0.00927286991461
ip & device: 0.00694314823037
ip & os: 0.0295303991247
ip & channel: 0.01910882176
ip & app_device: 0.0116509685587
ip & app_os: 0.0352817805537
ip & app_channel: 0.0269119027086
ip & device_os: 0.0319996095279
ip & device_channel: 0.0232395465844
ip & os_channel: 0.0687097193232
ip & hour: 0.00842828568901
app & device: 0.0278221472785
app & os: 0.0138230238585
app & channel: 0.269795263371
app & ip_device: 0.0147259939819
app & ip_os: 0.0259135610616
app & ip_channel: 0.104454990715
app & device_os: 0.0146956128532
app & device_channel: 0.263802502515
app & os_channel: 0.174031886398
app & hour: 0.00196613604054
device & os: 0.0292843937519
device & channel: 0.0190570407725
device & ip_app: 0.010875061967
device & ip_os: 0.0123530449439
device & ip_channel: 0.0109356373285
device & app_os: 0.0164432432512
device & app_channel: 0.0215004570006
device & os_channel: 0.0157067148459
device & hour: 0.00218613697383
os & channel: 0.0111228058317
os & ip_app: 0.0392854321075
os & ip_device: 0.036077091733
os & ip_channel: 0.0587870031998
os & app_device: 0.0155230901264
os & app_channel: 0.0127829864518
os & device_channel: 0.0152616221357
os & hour: 0.000521350840747
channel & ip_app: 0.105000757011
channel & ip_device: 0.0241785886481
channel & ip_os: 0.0498727607888
channel & app_device: 0.256371969166
channel & app_os: 0.148765039685
channel & device_os: 0.0125333204848
channel & hour: 0.00297988945705
ip_app & device_os: 0.0420245202447
ip_app & device_channel: 0.109073593121
ip_app & os_channel: 0.150919655929
ip_app & hour: 0.0116179491756
ip_device & app_os: 0.0412224118725
ip_device & app_channel: 0.0327801472947
ip_device & os_channel: 0.0755055123239
ip_device & hour: 0.00926587918155
ip_os & app_device: 0.0292665954443
ip_os & app_channel: 0.0654641563309
ip_os & device_channel: 0.0560870467462
ip_os & hour: 0.0302603369959
ip_channel & app_device: 0.105314833255
ip_channel & app_os: 0.136361783362
ip_channel & device_os: 0.0618911697116
ip_channel & hour: 0.0232839554938
app_device & os_channel: 0.169265661127
app_device & hour: 0.00226247457287
app_os & device_channel: 0.149640128183
app_os & hour: 0.00113844687176
app_channel & device_os: 0.0147866713056
app_channel & hour: 0.00478454682739
device_os & hour: 0.000885698518659
device_channel & hour: 0.00329861922203
os_channel & hour: 0.00227965522251