View Friedman-datasets fri_c2_1000_5 (public)

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Summary

Artificial data generated from the Friedman function, part of a collection of 80 data sets. This particular set has 5 features, 1 output (6th feature), colinearity degree 2, and 1000 instances.

License
unknown (from Weka repository)
Dependencies
Tags
arff slurped Weka
Attribute Types
Floating Point
Download
# Instances: 1000 / # Attributes: 6
HDF5 (57.4 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
arff
Name
fri_c2_1000_5
Version mldata
0
Comment
Names
oz1,oz2,oz3,oz4,oz5,oz6,
Types
  1. numeric
  2. numeric
  3. numeric
  4. numeric
  5. numeric
  6. numeric
Data (first 10 data points)
    oz1 oz2 oz3 oz4 oz5 oz6
    -0.91... -0.48... -0.90... -0.39... 1.48... 1.22...
    -1.09... -0.57... -0.20... 0.74... -0.81... 0.70...
    0.87... 0.24... -0.29... -0.09... -1.55... -1.48...
    -0.50... 0.09... 0.94... 1.00... 1.06... 1.09...
    -0.70... -1.36... -0.77... -1.59... 0.46... 0.05...
    0.89... 0.35... 1.23... 1.45... -0.07... -1.18...
    0.27... -0.40... 0.31... 0.72... -0.46... 0.83...
    1.47... 1.53... 2.60... 1.57... 1.60... 1.76...
    0.98... 1.20... 2.42... 1.13... -1.41... -0.80...
    -0.36... -0.47... 0.80... 1.04... -0.24... 0.93...
    ... ... ... ... ... ...
Description

A zip file containing 80 artificial datasets generated from the Friedman function donated by Dr. M. Fatih Amasyali (Yildiz Technical Unversity) (Friedman-datasets.zip, 5,802,204 Bytes)

URLs
(No information yet)
Publications
    Data Source
    http://www.ce.yildiz.edu.tr/en/myindex.php?id=14 http://www.ce.yildiz.edu.tr/
    Measurement Details
    Usage Scenario
    revision 1
    by mldata on 2010-11-06 10:01
    revision 2
    by jaakkopeltonen on 2010-11-22 18:40
    revision 3
    by jaakkopeltonen on 2010-11-22 18:40

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    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.

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    Acknowledgements

    This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
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