machine-learning.data-sets: load commonly used test data sets.
John Benediktsson
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! Copyright (C) 2012 John Benediktsson
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! See http://factorcode.org/license.txt for BSD license
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USING: assocs csv io.encodings.utf8 io.files kernel math.parser
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sequences ;
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IN: machine-learning.data-sets
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TUPLE: data-set data target target-names description
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feature-names ;
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C: <data-set> data-set
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<PRIVATE
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: load-file ( name -- contents )
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"resource:extra/machine-learning/data-sets/" prepend
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utf8 file-contents ;
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PRIVATE>
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: load-iris ( -- data-set )
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"iris.csv" load-file string>csv unclip [
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[
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unclip-last
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[ [ string>number ] map ]
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[ string>number ] bi*
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] { } map>assoc unzip
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] [ 2 tail ] bi*
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"iris.rst" load-file
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{
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"sepal length (cm)" "sepal width (cm)"
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"petal length (cm)" "petal width (cm)"
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} <data-set> ;
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@ -0,0 +1,151 @@
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150,4,setosa,versicolor,virginica
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5.1,3.5,1.4,0.2,0
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4.9,3.0,1.4,0.2,0
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4.7,3.2,1.3,0.2,0
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4.6,3.1,1.5,0.2,0
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5.0,3.6,1.4,0.2,0
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5.4,3.9,1.7,0.4,0
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4.6,3.4,1.4,0.3,0
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5.0,3.4,1.5,0.2,0
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4.4,2.9,1.4,0.2,0
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4.9,3.1,1.5,0.1,0
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5.4,3.7,1.5,0.2,0
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4.8,3.4,1.6,0.2,0
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4.8,3.0,1.4,0.1,0
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4.3,3.0,1.1,0.1,0
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5.8,4.0,1.2,0.2,0
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5.7,4.4,1.5,0.4,0
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5.4,3.9,1.3,0.4,0
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5.1,3.5,1.4,0.3,0
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5.7,3.8,1.7,0.3,0
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5.1,3.8,1.5,0.3,0
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5.4,3.4,1.7,0.2,0
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5.1,3.7,1.5,0.4,0
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4.6,3.6,1.0,0.2,0
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5.1,3.3,1.7,0.5,0
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4.8,3.4,1.9,0.2,0
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5.0,3.0,1.6,0.2,0
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5.0,3.4,1.6,0.4,0
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5.2,3.5,1.5,0.2,0
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5.2,3.4,1.4,0.2,0
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4.7,3.2,1.6,0.2,0
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4.8,3.1,1.6,0.2,0
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5.4,3.4,1.5,0.4,0
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5.2,4.1,1.5,0.1,0
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5.5,4.2,1.4,0.2,0
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4.9,3.1,1.5,0.1,0
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5.0,3.2,1.2,0.2,0
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5.5,3.5,1.3,0.2,0
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4.9,3.1,1.5,0.1,0
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4.4,3.0,1.3,0.2,0
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5.1,3.4,1.5,0.2,0
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5.0,3.5,1.3,0.3,0
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4.5,2.3,1.3,0.3,0
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4.4,3.2,1.3,0.2,0
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5.0,3.5,1.6,0.6,0
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5.1,3.8,1.9,0.4,0
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4.8,3.0,1.4,0.3,0
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5.1,3.8,1.6,0.2,0
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4.6,3.2,1.4,0.2,0
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5.3,3.7,1.5,0.2,0
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5.0,3.3,1.4,0.2,0
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7.0,3.2,4.7,1.4,1
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6.4,3.2,4.5,1.5,1
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6.9,3.1,4.9,1.5,1
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5.5,2.3,4.0,1.3,1
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6.5,2.8,4.6,1.5,1
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5.7,2.8,4.5,1.3,1
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6.3,3.3,4.7,1.6,1
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4.9,2.4,3.3,1.0,1
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6.6,2.9,4.6,1.3,1
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5.2,2.7,3.9,1.4,1
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5.0,2.0,3.5,1.0,1
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5.9,3.0,4.2,1.5,1
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6.0,2.2,4.0,1.0,1
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6.1,2.9,4.7,1.4,1
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5.6,2.9,3.6,1.3,1
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6.7,3.1,4.4,1.4,1
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5.6,3.0,4.5,1.5,1
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5.8,2.7,4.1,1.0,1
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6.2,2.2,4.5,1.5,1
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5.6,2.5,3.9,1.1,1
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5.9,3.2,4.8,1.8,1
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6.1,2.8,4.0,1.3,1
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6.3,2.5,4.9,1.5,1
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6.1,2.8,4.7,1.2,1
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6.4,2.9,4.3,1.3,1
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6.6,3.0,4.4,1.4,1
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6.8,2.8,4.8,1.4,1
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6.7,3.0,5.0,1.7,1
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6.0,2.9,4.5,1.5,1
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5.7,2.6,3.5,1.0,1
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5.5,2.4,3.8,1.1,1
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5.5,2.4,3.7,1.0,1
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5.8,2.7,3.9,1.2,1
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6.0,2.7,5.1,1.6,1
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5.4,3.0,4.5,1.5,1
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6.0,3.4,4.5,1.6,1
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6.7,3.1,4.7,1.5,1
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6.3,2.3,4.4,1.3,1
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5.6,3.0,4.1,1.3,1
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5.5,2.5,4.0,1.3,1
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5.5,2.6,4.4,1.2,1
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6.1,3.0,4.6,1.4,1
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5.8,2.6,4.0,1.2,1
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5.0,2.3,3.3,1.0,1
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5.6,2.7,4.2,1.3,1
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5.7,3.0,4.2,1.2,1
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5.7,2.9,4.2,1.3,1
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6.2,2.9,4.3,1.3,1
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5.1,2.5,3.0,1.1,1
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5.7,2.8,4.1,1.3,1
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6.3,3.3,6.0,2.5,2
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5.8,2.7,5.1,1.9,2
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7.1,3.0,5.9,2.1,2
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6.3,2.9,5.6,1.8,2
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6.5,3.0,5.8,2.2,2
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7.6,3.0,6.6,2.1,2
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4.9,2.5,4.5,1.7,2
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7.3,2.9,6.3,1.8,2
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6.7,2.5,5.8,1.8,2
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7.2,3.6,6.1,2.5,2
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6.5,3.2,5.1,2.0,2
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6.4,2.7,5.3,1.9,2
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6.8,3.0,5.5,2.1,2
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5.7,2.5,5.0,2.0,2
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5.8,2.8,5.1,2.4,2
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6.4,3.2,5.3,2.3,2
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6.5,3.0,5.5,1.8,2
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7.7,3.8,6.7,2.2,2
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7.7,2.6,6.9,2.3,2
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6.0,2.2,5.0,1.5,2
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6.9,3.2,5.7,2.3,2
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5.6,2.8,4.9,2.0,2
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7.7,2.8,6.7,2.0,2
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6.3,2.7,4.9,1.8,2
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6.7,3.3,5.7,2.1,2
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7.2,3.2,6.0,1.8,2
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6.2,2.8,4.8,1.8,2
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6.1,3.0,4.9,1.8,2
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6.4,2.8,5.6,2.1,2
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7.2,3.0,5.8,1.6,2
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7.4,2.8,6.1,1.9,2
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7.9,3.8,6.4,2.0,2
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6.4,2.8,5.6,2.2,2
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6.3,2.8,5.1,1.5,2
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6.1,2.6,5.6,1.4,2
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7.7,3.0,6.1,2.3,2
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6.3,3.4,5.6,2.4,2
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6.4,3.1,5.5,1.8,2
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6.0,3.0,4.8,1.8,2
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6.9,3.1,5.4,2.1,2
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6.7,3.1,5.6,2.4,2
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6.9,3.1,5.1,2.3,2
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5.8,2.7,5.1,1.9,2
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6.8,3.2,5.9,2.3,2
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6.7,3.3,5.7,2.5,2
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6.7,3.0,5.2,2.3,2
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6.3,2.5,5.0,1.9,2
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6.5,3.0,5.2,2.0,2
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6.2,3.4,5.4,2.3,2
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5.9,3.0,5.1,1.8,2
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@ -0,0 +1,59 @@
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Iris Plants Database
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Notes
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-----
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Data Set Characteristics:
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:Number of Instances: 150 (50 in each of three classes)
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:Number of Attributes: 4 numeric, predictive attributes and the class
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:Attribute Information:
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- sepal length in cm
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- sepal width in cm
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- petal length in cm
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- petal width in cm
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- class:
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- Iris-Setosa
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- Iris-Versicolour
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- Iris-Virginica
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:Summary Statistics:
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============== ==== ==== ======= ===== ====================
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Min Max Mean SD Class Correlation
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============== ==== ==== ======= ===== ====================
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sepal length: 4.3 7.9 5.84 0.83 0.7826
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sepal width: 2.0 4.4 3.05 0.43 -0.4194
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petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
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petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
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============== ==== ==== ======= ===== ====================
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:Missing Attribute Values: None
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:Class Distribution: 33.3% for each of 3 classes.
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:Creator: R.A. Fisher
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:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
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:Date: July, 1988
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This is a copy of UCI ML iris datasets.
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http://archive.ics.uci.edu/ml/datasets/Iris
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The famous Iris database, first used by Sir R.A Fisher
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This is perhaps the best known database to be found in the
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pattern recognition literature. Fisher's paper is a classic in the field and
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is referenced frequently to this day. (See Duda & Hart, for example.) The
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data set contains 3 classes of 50 instances each, where each class refers to a
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type of iris plant. One class is linearly separable from the other 2; the
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latter are NOT linearly separable from each other.
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References
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----------
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- Fisher,R.A. "The use of multiple measurements in taxonomic problems"
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Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
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Mathematical Statistics" (John Wiley, NY, 1950).
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- Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
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(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
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- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
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Structure and Classification Rule for Recognition in Partially Exposed
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Environments". IEEE Transactions on Pattern Analysis and Machine
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Intelligence, Vol. PAMI-2, No. 1, 67-71.
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- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
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on Information Theory, May 1972, 431-433.
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- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
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conceptual clustering system finds 3 classes in the data.
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- Many, many more ...
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