Data
iris_reproduced

iris_reproduced

active ARFF Publicly available Visibility: public Uploaded 09-07-2022 by Laurens Krudde
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Author: R.A. Fisher Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Iris) - 1936 - Donated by Michael Marshall Please cite: Iris Plants Database This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. Predicted attribute: class of iris plant. This is an exceedingly simple domain. ### Attribute Information: 1. sepal length in cm 2. sepal width in cm 3. petal length in cm 4. petal width in cm 5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica From OpenML: https://www.openml.org/d/61

5 features

class (target)nominal3 unique values
0 missing
sepallengthnumeric35 unique values
0 missing
sepalwidthnumeric23 unique values
0 missing
petallengthnumeric43 unique values
0 missing
petalwidthnumeric22 unique values
0 missing

19 properties

150
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
4
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.99
Average class difference between consecutive instances.
0
Percentage of missing values.
0.03
Number of attributes divided by the number of instances.
80
Percentage of numeric attributes.
33.33
Percentage of instances belonging to the most frequent class.
20
Percentage of nominal attributes.
50
Number of instances belonging to the most frequent class.
33.33
Percentage of instances belonging to the least frequent class.
50
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

2 tasks

5 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
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