Data
iris

iris

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
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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

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

62 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.
3
The minimal number of distinct values among attributes of the nominal type.
80
Percentage of numeric attributes.
5.32
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
-0.27
Minimum skewness among attributes of the numeric type.
20
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
3
The maximum number of distinct values among attributes of the nominal type.
0.33
Maximum skewness among attributes of the numeric type.
0.43
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.33
Third quartile of skewness among attributes of the numeric type.
1.76
Maximum standard deviation of attributes of the numeric type.
33.33
Percentage of instances belonging to the least frequent class.
-1.39
First quartile of kurtosis among attributes of the numeric type.
1.53
Third quartile of standard deviation of attributes of the numeric type.
Average entropy of the attributes.
50
Number of instances belonging to the least frequent class.
1.66
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
-0.75
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
3.46
Mean of means among attributes of the numeric type.
-0.23
First quartile of skewness among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.52
First quartile of standard deviation of attributes of the numeric type.
0.99
Average class difference between consecutive instances.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
1.58
Entropy of the target attribute values.
3
Average number of distinct values among the attributes of the nominal type.
-0.95
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.03
Number of attributes divided by the number of instances.
0.07
Mean skewness among attributes of the numeric type.
3.41
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.95
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
33.33
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
0.1
Second quartile (Median) of skewness among attributes of the numeric type.
50
Number of instances belonging to the most frequent class.
-1.4
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.8
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
1.2
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.29
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0.08
Third quartile of kurtosis among attributes of the numeric type.
5.84
Maximum of means among attributes of the numeric type.

30 tasks

4580 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
473 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class - cost matrix: [[0,10,15],[20,0,25],[10,10,0]]
394 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
345 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
329 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
278 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: matthews_correlation_coefficient - target_feature: class
32 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
3 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
0 runs - estimation_procedure: 4-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: class
173 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
82 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
10 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
60 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - evaluation_measure: recall, precision - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - target_feature: class
0 runs - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - target_feature: ii
0 runs - target_feature: find something
1298 runs - target_feature: class
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