Data
iris

iris

active ARFF Publicly available Visibility: public Uploaded 06-04-2014 by Jan van Rijn
0 likes downloaded by 0 people , 0 total downloads 0 issues 0 downvotes
  • study_927 study_2907 study_3029 study_10889 study_11301 study_2079 study_4146 study_4312 study_6936 study_1256 study_1502 study_11894 study_13073 study_13132 study_1055 study_4714 study_10340 study_13007 study_463 study_688 study_705 study_2771 study_6235 study_6416 study_7294 study_13369 study_153 study_3178 study_4067 study_4812 study_12756 study_705 study_2056 study_4542 study_10607 study_11961 study_3344 study_4067 study_5657 study_6235 study_10111 study_12080 study_1276 study_2771 study_3156 study_3582 study_11587 study_1782 study_3484 study_4291 study_2599 study_5402 study_12913
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit

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

107 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.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3
Average number of distinct values among the attributes of the nominal type.
-0.23
First quartile of skewness among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.07
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.07
Mean skewness among attributes of the numeric type.
0.52
First quartile of standard deviation of attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
33.33
Percentage of instances belonging to the most frequent class.
0.95
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.58
Entropy of the target attribute values.
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
50
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.95
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.4
Minimum kurtosis among attributes of the numeric type.
3.41
Second quartile (Median) of means among attributes of the numeric type.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.33
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.29
Maximum kurtosis among attributes of the numeric type.
1.2
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
5.84
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.1
Second quartile (Median) of skewness among attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.03
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.8
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3
The maximum number of distinct values among attributes of the nominal type.
-0.27
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
0.08
Third quartile of kurtosis among attributes of the numeric type.
0.99
Average class difference between consecutive instances.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.33
Maximum skewness among attributes of the numeric type.
0.43
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
5.32
Third quartile of means among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.76
Maximum standard deviation of attributes of the numeric type.
33.33
Percentage of instances belonging to the least frequent class.
80
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.07
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
50
Number of instances belonging to the least frequent class.
20
Percentage of nominal attributes.
0.33
Third quartile of skewness among attributes of the numeric type.
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.75
Mean kurtosis among attributes of the numeric type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.53
Third quartile of standard deviation of attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.46
Mean of means among attributes of the numeric type.
0.03
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.39
First quartile of kurtosis among attributes of the numeric type.
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.07
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.66
First quartile of means among attributes of the numeric type.

12 tasks

44 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
2 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: average_cost - target_feature: class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
0 runs - estimation_procedure: Test on Training Data - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
0 runs - estimation_procedure: Leave one out - target_feature: class
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Define a new task