Data
Click_prediction_small

Click_prediction_small

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Author: Tencent Inc. Source: [KDD Cup](https://www.kddcup2012.org/) - 2012 Please cite: This data set is the same as version 4, but has additional unlabeled data attached to it. This is meant for a machine learning challenge. The complete labeled version of this dataset is version 6 (but this version is kept private for the duration of the challenge). 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
399482 missing
impressionnumeric327 unique values
0 missing
url_hash (ignore)numeric15787 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_id (ignore)numeric248486 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

107 properties

798964
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
399482
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.
1.76
Second quartile (Median) of skewness among attributes of the numeric type.
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
15975651.36
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
100889.02
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
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 binary attributes.
Third quartile of entropy among attributes.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-0.87
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
38.16
Third quartile of kurtosis among attributes of the numeric type.
0.36
Average class difference between consecutive instances.
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
213.6
Maximum skewness among attributes of the numeric type.
0.63
Minimum standard deviation of attributes of the numeric type.
4.17
Percentage of missing values.
1930301.79
Third quartile of means among attributes of the numeric type.
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
7227138.46
Maximum standard deviation of attributes of the numeric type.
8.4
Percentage of instances belonging to the least frequent class.
91.67
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
67089
Number of instances belonging to the least frequent class.
8.33
Percentage of nominal attributes.
5.52
Third quartile of skewness among attributes of the numeric type.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
6629.22
Mean kurtosis among attributes of the numeric type.
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
2979990.45
Third quartile of standard deviation of attributes of the numeric type.
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2222503.01
Mean of means among attributes of the numeric type.
0.18
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.93
First quartile of kurtosis among attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.16
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.25
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.04
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.92
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.12
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.54
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
-0.28
First quartile of skewness among attributes of the numeric type.
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.16
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
17.37
First quartile of standard deviation of attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.16
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.07
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
25.63
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.25
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
50
Percentage of instances belonging to the most frequent class.
1513484.32
Mean standard deviation of attributes of the numeric type.
2.33
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.42
Entropy of the target attribute values.
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
399482
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
35086.02
Second quartile (Median) of means among attributes of the numeric type.
0.57
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.53
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.04
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.17
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
59561.84
Maximum kurtosis among attributes of the numeric type.
1.46
Minimum of means among attributes of the numeric type.

5 tasks

0 runs - estimation_procedure: 33% Holdout set - target_feature: click
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: Holdout unlabeled - evaluation_measure: area_under_roc_curve - target_feature: click - cost matrix: [[0,10],[100,0]]
Define a new task