Task
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abalone-regression-task - submitted by demo 666 views, 725 downloads, 0 comments
last edited by demo - Dec 3, 2010, 21:25 CET Rating




- Summary:
A simple regression task on the Abalone data
- License: CC-BY-SA 3.0
- Tags: abalone Regression
- Performance Measure: Root Mean Squared Error
- Task Type: Regression
- Methods / Challenges: 0 methods, 0 challenges
- Download: HDF5 (50.8 KB) XML Matlab Octave
- Files are converted on demand and the process can take up to a minute. Please wait until download begins.
- Summary:
A simple regression task on the Abalone data
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boston housing regression - submitted by cong 1377 views, 630 downloads, 0 comments
last edited by cong - Nov 28, 2010, 16:04 CET Rating




- Summary:
The standard task for the boston housing dataset
- License: CC-BY-SA 3.0
- Tags: boston housing Regression uci
- Performance Measure: Root Mean Squared Error
- Task Type: Regression
- Methods / Challenges: 1 methods, 0 challenges
- Download: HDF5 (16.4 KB) XML Matlab Octave
- Summary:
The standard task for the boston housing dataset
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boston housing scaled regression - submitted by cong 437 views, 630 downloads, 0 comments
last edited by cong - Dec 2, 2010, 17:38 CET Rating




- Summary:
The standard task for the boston housing dataset
- License: CC-BY-SA 3.0
- Tags: boston housing Regression scaled uci
- Performance Measure: Root Mean Squared Error
- Task Type: Regression
- Methods / Challenges: 1 methods, 0 challenges
- Download: HDF5 (16.4 KB) XML Matlab Octave
- Summary:
The standard task for the boston housing dataset
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Sweets recommender system - submitted by kidzik 995 views, 360 downloads, 0 comments
last edited by kidzik - Sep 27, 2011, 11:18 CET Rating




- Summary:
Ratings prediction basing on other users' votes.
- License: CC-BY-SA 3.0
- Tags: collaborative-filtering Prediction recommender Regression sweetrs sweets
- Performance Measure: Mean Absolute Error
- Task Type: Regression
- Methods / Challenges: 1 methods, 0 challenges
- Download: HDF5 (28.2 KB) XML Matlab Octave
- Summary:
Ratings prediction basing on other users' votes.
Disclaimer
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Acknowledgements
This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
http://www.pascal-network.org/.
