View uci-20070111 2dplanes (public)

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Summary

(No information yet)

License
unknown (from Weka repository)
Dependencies
Tags
arff slurped Weka
Attribute Types
Integer,Floating Point
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# Instances: 40768 / # Attributes: 11
HDF5 (1.9 MB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format
arff
Name
2dplanes
Version mldata
0
Comment

This is an artificial data set described in Breiman et al. (1984,p.238) (with variance 1 instead of 2).

Generate the values of the 10 attributes independently using the following probabilities:

P(X_1 = -1) = P(X_1 = 1) = 1/2 P(X_m = -1) = P(X_m = 0) = P(X_m = 1) = 1/3, m=2,...,10

Obtain the value of the target variable Y using the rule:

if X_1 = 1 set Y = 3 + 3X_2 + 2X_3 + X_4 + sigma(0,1) if X_1 = -1 set Y = -3 + 3X_5 + 2X_6 + X_7 + sigma(0,1)

Characteristics: 40768 cases, 11 continuous attributes Source: collection of regression datasets by Luis Torgo (ltorgo@ncc.up.pt) at http://www.ncc.up.pt/~ltorgo/Regression/DataSets.html Original source: Breiman et al. (1984, p.238).

Names
x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,
Types
  1. numeric
  2. numeric
  3. numeric
  4. numeric
  5. numeric
  6. numeric
  7. numeric
  8. numeric
  9. numeric
  10. numeric
Data (first 10 data points)
    x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 ...
    1 1 1 1 -1 0 1 0 -1 1 ...
    -1 0 -1 -1 1 1 1 0 -1 0 ...
    1 0 1 1 -1 -1 0 1 1 1 ...
    1 0 0 1 0 -1 1 1 1 -1 ...
    -1 0 -1 -1 -1 -1 -1 1 1 -1 ...
    1 1 1 -1 -1 -1 -1 1 -1 -1 ...
    1 -1 0 0 -1 0 0 0 0 0 ...
    1 -1 0 -1 1 1 1 -1 1 0 ...
    -1 0 -1 -1 -1 1 0 1 -1 1 ...
    -1 1 -1 0 1 0 -1 0 -1 1 ...
    ... ... ... ... ... ... ... ... ... ... ...
Description

A gzip'ed tar containing UCI and UCI KDD datasets (uci-20070111.tar.gz, 17,952,832 Bytes)

URLs
(No information yet)
Publications
    Data Source
    http://www.ics.uci.edu/~mlearn/MLRepository.html http://kdd.ics.uci.edu/
    Measurement Details
    Usage Scenario
    revision 1
    by mldata on 2010-11-06 09:59

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