Public Archive Task
-
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
-
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
-
iris multiclass - submitted by cong 462 views, 633 downloads, 0 comments
last edited by cong - Nov 28, 2010, 15:48 CET Rating
- Summary:
The standard task for the iris dataset
- License: CC-BY-SA 3.0
- Tags: demo multiclass
- Performance Measure: Accuracy
- Task Type: Multi Class Classification
- Methods / Challenges: 2 methods, 1 challenges
- Download: HDF5 (12.9 KB) XML Matlab Octave
- Summary:
The standard task for the iris dataset
-
pima binclass roc - submitted by cong 739 views, 700 downloads, 0 comments
last edited by cong - Nov 28, 2010, 15:32 CET Rating
- Summary:
The standard task for the pima indian diabetes binary classification dataset
- License: CC-BY-SA 3.0
- Tags: binary Classification diabetes pima roc
- Performance Measure: ROC Curve
- Task Type: Binary Classification
- Methods / Challenges: 1 methods, 0 challenges
- Download: HDF5 (20.8 KB) XML Matlab Octave
- Summary:
The standard task for the pima indian diabetes binary classification dataset
-
pima binclass - submitted by cong 700 views, 719 downloads, 0 comments
last edited by cong - Nov 28, 2010, 14:44 CET Rating
- Summary:
The standard task for the pima indian diabetes binary classification dataset
- License: CC-BY-SA 3.0
- Tags: binary Classification diabetes pima
- Performance Measure: Accuracy
- Task Type: Binary Classification
- Methods / Challenges: 1 methods, 1 challenges
- Download: HDF5 (20.8 KB) XML Matlab Octave
- Summary:
The standard task for the pima indian diabetes binary classification dataset
-
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
-
Diabetes IDA Classification - submitted by sonne 1256 views, 862 downloads, 0 comments
last edited by demo - Dec 3, 2010, 15:25 CET Rating
- Summary:
The diabetes task from the IDA Benchmark repository.
- License: CC-BY-SA 3.0
- Tags: Classification diabetes IDA_Benchmark_Repository
- Performance Measure: Accuracy
- Task Type: Binary Classification
- Methods / Challenges: 0 methods, 0 challenges
- Download: HDF5 (389.1 KB) XML Matlab Octave
- Files are converted on demand and the process can take up to a minute. Please wait until download begins.
- Summary:
The diabetes task from the IDA Benchmark repository.
-
Banana classification - submitted by demo 1710 views, 822 downloads, 0 comments
last edited by demo - Dec 2, 2010, 14:17 CET Rating
- Summary:
Simple demo classification task on the banana dataset
- License: CC-BY-SA 3.0
- Tags: banana Classification demo
- Performance Measure: Accuracy
- Task Type: Binary Classification
- Methods / Challenges: 1 methods, 0 challenges
- Download: HDF5 (52.8 KB) XML Matlab Octave
- Files are converted on demand and the process can take up to a minute. Please wait until download begins.
- Summary:
Simple demo classification task on the banana dataset
-
Multi-Task Classification - submitted by jeanbaptiste 226 views, 189 downloads, 0 comments
last edited by jeanbaptiste - Mar 13, 2012, 15:27 CET Rating
- Summary:
Learn simultaneously two mult-class classification tasks. , the first is to classify documents from Yahoo! web directory and the second is to classifiy documents from DMOZ web directory. Labels are different amog taks but the tasks are related
- License: CC-BY-SA 3.0
- Tags: Classification DMOZ multi-class multi-task text web-pages Yahoo!
- Performance Measure: Accuracy
- Task Type: Multi Class Classification
- Methods / Challenges: 0 methods, 0 challenges
- Download: HDF5 (10.7 KB) XML Matlab Octave
- Summary:
Learn simultaneously two mult-class classification tasks. , the first is to classify documents from Yahoo! web directory and the second is to classifiy documents from DMOZ web directory. Labels are different amog taks but the tasks are related
-
Jokes ratings prediction - submitted by kidzik 852 views, 426 downloads, 0 comments
last edited by kidzik - Sep 29, 2011, 12:18 CET Rating
- Summary:
Collaborative filtering task for jokes
- License: CC-BY-SA 3.0
- Performance Measure: Accuracy
- Task Type: Regression
- Methods / Challenges: 0 methods, 0 challenges
- Download: HDF5 (82.4 KB) XML Matlab Octave
- Files are converted on demand and the process can take up to a minute. Please wait until download begins.
- Summary:
Collaborative filtering task for jokes
Disclaimer
We are acting in good faith to make datasets submitted for the use of the scientific community available to everybody, but if you are a copyright holder and would like us to remove a dataset please inform us and we will do it as soon as possible.
Acknowledgements
This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
http://www.pascal-network.org/.