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Click_prediction_small

Click_prediction_small

active ARFF Publicly available Visibility: public Uploaded 24-11-2014 by Joaquin Vanschoren
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Author: Tencent Inc. Source: [KDD Cup](https://www.kddcup2012.org/) - 2012 Please cite: This data is derived from the 2012 KDD Cup. The data is subsampled to 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
impressionnumeric327 unique values
0 missing
url_hashnumeric15787 unique values
0 missing
ad_idnumeric82778 unique values
0 missing
advertiser_idnumeric11955 unique values
0 missing
depthnumeric3 unique values
0 missing
positionnumeric3 unique values
0 missing
query_idnumeric248486 unique values
0 missing
keyword_idnumeric93414 unique values
0 missing
title_idnumeric155012 unique values
0 missing
description_idnumeric129271 unique values
0 missing
user_idnumeric287967 unique values
0 missing

19 properties

399482
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.
0
Percentage of instances having missing values.
0.72
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
91.67
Percentage of numeric attributes.
83.21
Percentage of instances belonging to the most frequent class.
8.33
Percentage of nominal attributes.
332393
Number of instances belonging to the most frequent class.
16.79
Percentage of instances belonging to the least frequent class.
67089
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

15 tasks

313 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: click - cost matrix: [[0,10],[100,0]]
0 runs - estimation_procedure: 33% Holdout set - target_feature: click
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: click
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: click
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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