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iris

iris

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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
Show all 5 features

107 properties

NumberOfInstances
150
Number of instances (rows) of the dataset.
NumberOfFeatures
5
Number of attributes (columns) of the dataset.
NumberOfClasses
3
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
4
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.99
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
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
CfsSubsetEval_DecisionStumpErrRate
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
CfsSubsetEval_DecisionStumpKappa
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
CfsSubsetEval_NaiveBayesAUC
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
CfsSubsetEval_NaiveBayesErrRate
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
CfsSubsetEval_NaiveBayesKappa
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
CfsSubsetEval_kNN1NAUC
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
CfsSubsetEval_kNN1NErrRate
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
CfsSubsetEval_kNN1NKappa
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
ClassEntropy
1.58
Entropy of the target attribute values.
DecisionStumpAUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.03
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
33.33
Percentage of instances belonging to the most frequent class.
MajorityClassSize
50
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.29
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
5.84
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
3
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.33
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1.76
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.75
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
3.46
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
3
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.07
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.95
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.4
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
1.2
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
3
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.27
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.43
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
33.33
Percentage of instances belonging to the least frequent class.
MinorityClassSize
50
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
80
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
20
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.39
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
1.66
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.23
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.52
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.95
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
3.41
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.1
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.8
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.08
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
5.32
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.33
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1.53
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.9
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

58 tasks

Supervised Classification on iris
4443 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on iris
478 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class - cost matrix: [[0,10,15],[20,0,25],[10,10,0]]
Supervised Classification on iris
394 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on iris
345 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on iris
329 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on iris
280 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: matthews_correlation_coefficient - target_feature: class
Supervised Classification on iris
32 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
Supervised Classification on iris
3 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on iris
1 runs - estimation_procedure: 33% Holdout set - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: 4-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: c_index - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: confusion_matrix - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: Leave one out - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: 10% Holdout set - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: Leave one out - evaluation_measure: mean_absolute_error - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: root_mean_squared_error - target_feature: class
Supervised Classification on iris
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: class
Supervised Regression on iris
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: sepalwidth
Supervised Regression on iris
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: sepalwidth
Supervised Regression on iris
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: sepallength
Learning Curve on iris
173 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on iris
82 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on iris
10 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on iris
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on iris
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on iris
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on iris
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on iris
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on iris
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on iris
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on iris
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
Learning Curve on iris
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: class
Learning Curve on iris
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Supervised Data Stream Classification on iris
60 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Supervised Data Stream Classification on iris
0 runs - estimation_procedure: Interleaved Test then Train - evaluation_measure: recall, precision - target_feature: class
Clustering on iris
0 runs - target_feature: class
Clustering on iris
0 runs
Clustering on iris
0 runs - target_feature: Class
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering
Clustering on iris
0 runs - target_feature: ii
Clustering on iris
0 runs - target_feature: find something
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering - target_feature: class
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering - target_feature: Species
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering - target_feature: Euclidean Distance
Clustering on iris
0 runs - estimation_procedure: 50 times Clustering - target_feature: class
Subgroup Discovery on iris
1298 runs - target_feature: class
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