Data
Click_prediction_small

Click_prediction_small

active ARFF Publicly available Visibility: public Uploaded 27-11-2014 by Joaquin Vanschoren
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Author: Tencent Inc. Source: [KDD Cup](https://www.kddcup2012.org/) - 2012 Please cite: 0.1% balanced subsample of the original KDD dataset This data is derived from the 2012 KDD Cup. The data is subsampled to 0.1% of the original number of instances, downsampling the majority class (click=0) so that the target feature is reasonably balanced (5 to 1). The data is about advertisements shown alongside search results in a search engine, and whether or not people clicked on these ads. The task is to build the best possible model to predict whether a user will click on a given ad. A search session contains information on user id, the query issued by the user, ads displayed to the user, and target feature indicating whether a user clicked at least one of the ads in this session. The number of ads displayed to a user in a session is called ‘depth’. The order of an ad in the displayed list is called ‘position’. An ad is displayed as a short text called ‘title’, followed by a slightly longer text called ’description’, and a URL called ‘display URL’. To construct this dataset each session was split into multiple instances. Each instance describes an ad displayed under a certain setting (‘depth’, ‘position’). Instances with the same user id, ad id, query, and setting are merged. Each ad and each user have some additional properties located in separate data files that can be looked up using ids in the instances. The dataset has the following features: * Click – binary variable indicating whether a user clicked on at least one ad. * Impression - the number of search sessions in which AdID was impressed by UserID who issued Query. * Url_hash - URL is hashed for anonymity * AdID * AdvertiserID - some advertisers consistently optimize their ads, so the title and description of their ads are more attractive than those of others’ ads. * Depth - number of ads displayed to a user in a session * Position - order of an ad in the displayed list * QueryID - is the key of the data file 'queryid_tokensid.txt'. (follow the link to the original KDD Cup page, track 2) * KeywordID - is the key of 'purchasedkeyword_tokensid.txt' (follow the link to the original KDD Cup page, track 2) * TitleID - is the key of 'titleid_tokensid.txt' * DescriptionID - is the key of 'descriptionid_tokensid.txt' (follow the link to the original KDD Cup page, track 2) * UserID – is also the key of 'userid_profile.txt' (follow the link to the original KDD Cup page, track 2). 0 is a special value denoting that the user could be identified.

12 features

click (target)nominal2 unique values
0 missing
impressionnumeric99 unique values
0 missing
url_hash (ignore)numeric6941 unique values
0 missing
ad_idnumeric19228 unique values
0 missing
advertiser_idnumeric6064 unique values
0 missing
depthnumeric3 unique values
0 missing
positionnumeric3 unique values
0 missing
query_id (ignore)numeric30748 unique values
0 missing
keyword_idnumeric19803 unique values
0 missing
title_idnumeric25321 unique values
0 missing
description_idnumeric22381 unique values
0 missing
user_idnumeric30114 unique values
0 missing

62 properties

39948
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
11
Number of numeric attributes.
1
Number of nominal attributes.
8.33
Percentage of binary attributes.
100914.82
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.05
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
27130.69
Maximum kurtosis among attributes of the numeric type.
1.46
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
38.2
Third quartile of kurtosis among attributes of the numeric type.
16016715.9
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
91.67
Percentage of numeric attributes.
1921452.58
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
8.33
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0.88
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
5.52
Third quartile of skewness among attributes of the numeric type.
159.06
Maximum skewness among attributes of the numeric type.
0.63
Minimum standard deviation of attributes of the numeric type.
-0.93
First quartile of kurtosis among attributes of the numeric type.
2978866.63
Third quartile of standard deviation of attributes of the numeric type.
7222259.54
Maximum standard deviation of attributes of the numeric type.
16.84
Percentage of instances belonging to the least frequent class.
2.03
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
6728
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
3025.72
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
-0.28
First quartile of skewness among attributes of the numeric type.
2225380.71
Mean of means among attributes of the numeric type.
33.29
First quartile of standard deviation of attributes of the numeric type.
0.72
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
0.65
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2.4
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
35194.43
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
19.56
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
83.16
Percentage of instances belonging to the most frequent class.
1513460.57
Mean standard deviation of attributes of the numeric type.
1.78
Second quartile (Median) of skewness among attributes of the numeric type.
33220
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

15 tasks

39609 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: click - cost matrix: [[0,10],[100,0]]
21183 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: click
0 runs - estimation_procedure: 4-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: click
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: click
44 runs - estimation_procedure: 10-fold Learning Curve - target_feature: click
1299 runs - target_feature: click
1299 runs - target_feature: click
0 runs - target_feature: click
0 runs - target_feature: click
0 runs - target_feature: click
0 runs - target_feature: click
0 runs - target_feature: click
0 runs - target_feature: click
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