View datasets-UCI audiology (public)























- Summary
(No information yet)
- License
- unknown (from Weka repository)
- Dependencies
- Tags
- arff slurped Weka
- Attribute Types
- String
- Download
-
# Instances: 226 / # Attributes: 70
HDF5 (666.8 KB) XML CSV ARFF LibSVM Matlab OctaveFiles are converted on demand and the process can take up to a minute. Please wait until download begins.
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- Original Data Format
- arff
- Name
- audiology
- Version mldata
- 0
- Comment
- Names
- age_gt_60,air,airBoneGap,ar_c,ar_u,bone,boneAbnormal,bser,history_buzzing,history_dizziness,
- Types
- nominal:f,t
- nominal:mild,moderate,normal,profound,severe
- nominal:f,t
- nominal:absent,elevated,normal
- nominal:absent,elevated,normal
- nominal:mild,moderate,normal,unmeasured
- nominal:f,t
- nominal:degraded,normal
- nominal:f,t
- nominal:f,t
- Data (first 10 data points)
age_... air airB... ar_c ar_u bone bone... bser hist... hist... ... t mild f normal normal unme... f nan f f ... t mild f normal normal unme... f nan f f ... t normal f normal normal unme... f nan f f ... t mild f normal normal unme... f nan f f ... t mild f normal normal unme... f nan f f ... t mild f normal normal unme... f nan f f ... t mild f normal normal unme... f nan f f ... t mild f normal normal unme... f nan f f ... f normal f normal normal unme... f nan f f ... f normal f normal normal unme... f nan f f ... ... ... ... ... ... ... ... ... ... ... ...
- Description
A jarfile containing 37 classification problems, originally obtained from the UCI repository (datasets-UCI.jar, 1,190,961 Bytes).
- URLs
- (No information yet)
- Publications
- Data Source
- http://www.ics.uci.edu/~mlearn/MLRepository.html
- Measurement Details
- Usage Scenario
- revision 1
- by mldata on 2010-11-06 09:57
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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/.
