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--- |
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task_categories: |
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- tabular-regression |
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- tabular-classification |
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tags: |
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- tabular |
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size_categories: |
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- 10K<n<100K |
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--- |
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## Source |
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Source: [UCI](https://archive.ics.uci.edu/ml/datasets/BlogFeedback) |
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## Data Set Information: |
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This data originates from blog posts. The raw HTML-documents |
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of the blog posts were crawled and processed. |
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The prediction task associated with the data is the prediction |
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of the number of comments in the upcoming 24 hours. In order |
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to simulate this situation, we choose a basetime (in the past) |
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and select the blog posts that were published at most |
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72 hours before the selected base date/time. Then, we calculate |
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all the features of the selected blog posts from the information |
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that was available at the basetime, therefore each instance |
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corresponds to a blog post. The target is the number of |
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comments that the blog post received in the next 24 hours |
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relative to the basetime. |
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In the train data, the basetimes were in the years |
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2010 and 2011. In the test data the basetimes were |
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in February and March 2012. This simulates the real-world |
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situtation in which training data from the past is available |
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to predict events in the future. |
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The train data was generated from different basetimes that may |
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temporally overlap. Therefore, if you simply split the train |
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into disjoint partitions, the underlying time intervals may |
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overlap. Therefore, the you should use the provided, temporally |
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disjoint train and test splits in order to ensure that the |
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evaluation is fair. |
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## Attribute Information: |
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1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared. |
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For example, myblog.blog.org would be the source of the post myblog.blog.org/post_2010_09_10 |
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51: Total number of comments before basetime |
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52: Number of comments in the last 24 hours before the |
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basetime |
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53: Let T1 denote the datetime 48 hours before basetime, |
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Let T2 denote the datetime 24 hours before basetime. |
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This attribute is the number of comments in the time period |
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between T1 and T2 |
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54: Number of comments in the first 24 hours after the |
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publication of the blog post, but before basetime |
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55: The difference of Attribute 52 and Attribute 53 |
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56...60: |
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The same features as the attributes 51...55, but |
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features 56...60 refer to the number of links (trackbacks), |
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while features 51...55 refer to the number of comments. |
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61: The length of time between the publication of the blog post |
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and basetime |
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62: The length of the blog post |
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63...262: |
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The 200 bag of words features for 200 frequent words of the |
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text of the blog post |
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263...269: binary indicator features (0 or 1) for the weekday |
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(Monday...Sunday) of the basetime |
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270...276: binary indicator features (0 or 1) for the weekday |
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(Monday...Sunday) of the date of publication of the blog |
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post |
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277: Number of parent pages: we consider a blog post P as a |
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parent of blog post B, if B is a reply (trackback) to |
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blog post P. |
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278...280: |
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Minimum, maximum, average number of comments that the |
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parents received |
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281: The target: the number of comments in the next 24 hours |
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(relative to basetime) |
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