jester.train:
training data
size: 3,000 x 10;
anonymous ratings of 10 jokes from 3,000 users;
one row per user, one column per joke;
ratings are real values ranging from -10.00 to +10.00 (the value \"nan\" corresponds to \"not rated\").
15,081 \"nan\" values (~50%)
you need to predict those \"nan\" values
jester10html.zip
text of 10 jokes
the titles correspond to the ID's of the jokes in jester.train above.
jester.id
please output your prediction on \"nan\" values in jester.train according to this order;
i.e. the first output should be your predicted value of jester.train(4,1), the second output should be jester.train(5,1), ...
jester.sample
a random baseline with random score between -10 and 10.
In information security domain, anomaly detection gains its own importance from researchers day by day. It is discovered that occasionally emerged frauds or intrusions in modern information systems have incurred significant loss when the suspicious activities were not detected or inefficiently processed. Therefore, numerous efforts have been devoted to devise effective detection methods, and then proceed to automation of anomaly identification in real time.
\r\nIn this practical course, we challenge students with a set of well designated learning tasks, which require the participants to understand the task scenario entirely and to design their own algorithms to identify corresponding anomalies.
\r\nThe competition on discovering the anomalies will be conducted based on the performance of their algorithms.
\r\nDepartment of computer science, Chair of IT Security
\r\nSee details in Homepage ADCG SS'14
\r\nWe thank Prof.Dr.Claudia Eckert for supporting us on this practical course.
", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "Evaluation", "content": "The evaluation metric for this competition is Mean Absolute Error.
\r\nFor every author in the dataset, the file should contain a header and have the following format:
\r\nId,Prediction", "isPublished": false, "hasIsPublished": false, "postTitle": "", "hasPostTitle": false, "mimeType": "", "hasMimeType": false }, { "name": "rules", "content": "\r\n\t\r\n
1,1.99
2,8.01
3,2.33
4,1.76
etc.
You cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.
\r\n\r\n Privately sharing code or data outside of teams is not permitted.\r\n It's okay to share code if made available to all participants on the forums.\r\n
\r\nTeam mergers are not allowed in this competition.
\r\n\r\nThe maximum size of a team is 2 participants.
\r\nYou may submit a maximum of 6 entries per day.
\r\nYou may select up to 2 final submissions for judging.
\r\nStart Date: 6/16/2014 2:20 AM UTC
\r\nMerger Deadline: None
\r\nEntry Deadline: None
\r\nEnd Date: 6/30/2014 11:59 PM UTC
\r\n\t\r\nVisibility
The competition is opened to public, although the general purpose is subject to a practical course hosted in Technische Universität München. All registered students are ought to submit their results online.
What is the team size?
The team can be formed up to two registered students.
What are the credits?
Only registered students in TUM can get credits in the end of competition. Non-participants of the practical course can as well get ranked by uploading results, if they are interested.
How are the credits are evaluated?
Participants (registered students) are required to take part in each session in class. They ought to present their results in public and also hand in a short report (maximal 2 pages). Credits are evaluated based on their performance and also the results.
How to avoid cheating?
Teams are required to complete the challenges individually. If teams present similar reports/results, they are forced to proceed to a check-phase to defend themselves.