Data
tecator

tecator

active ARFF Publicly available Visibility: public Uploaded 04-10-2014 by Joaquin Vanschoren
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  • binarized mythbusting_1 study_1 study_123 study_15 study_20 study_41 study_7 study_88
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Author: Source: Unknown - Date unknown Please cite: Binarized version of the original data set (see version 1). It converts the numeric target feature to a two-class nominal target feature by computing the mean and classifying all instances with a lower target value as positive ('P') and all others as negative ('N').

125 features

binaryClass (target)nominal2 unique values
0 missing
absorbance_1numeric216 unique values
0 missing
absorbance_2numeric216 unique values
0 missing
absorbance_3numeric216 unique values
0 missing
absorbance_4numeric214 unique values
0 missing
absorbance_5numeric216 unique values
0 missing
absorbance_6numeric216 unique values
0 missing
absorbance_7numeric216 unique values
0 missing
absorbance_8numeric216 unique values
0 missing
absorbance_9numeric216 unique values
0 missing
absorbance_10numeric216 unique values
0 missing
absorbance_11numeric216 unique values
0 missing
absorbance_12numeric215 unique values
0 missing
absorbance_13numeric216 unique values
0 missing
absorbance_14numeric216 unique values
0 missing
absorbance_15numeric216 unique values
0 missing
absorbance_16numeric216 unique values
0 missing
absorbance_17numeric216 unique values
0 missing
absorbance_18numeric216 unique values
0 missing
absorbance_19numeric215 unique values
0 missing
absorbance_20numeric216 unique values
0 missing
absorbance_21numeric215 unique values
0 missing
absorbance_22numeric216 unique values
0 missing
absorbance_23numeric216 unique values
0 missing
absorbance_24numeric216 unique values
0 missing
absorbance_25numeric215 unique values
0 missing
absorbance_26numeric216 unique values
0 missing
absorbance_27numeric216 unique values
0 missing
absorbance_28numeric216 unique values
0 missing
absorbance_29numeric215 unique values
0 missing
absorbance_30numeric216 unique values
0 missing
absorbance_31numeric216 unique values
0 missing
absorbance_32numeric215 unique values
0 missing
absorbance_33numeric216 unique values
0 missing
absorbance_34numeric216 unique values
0 missing
absorbance_35numeric215 unique values
0 missing
absorbance_36numeric216 unique values
0 missing
absorbance_37numeric216 unique values
0 missing
absorbance_38numeric216 unique values
0 missing
absorbance_39numeric216 unique values
0 missing
absorbance_40numeric215 unique values
0 missing
absorbance_41numeric216 unique values
0 missing
absorbance_42numeric216 unique values
0 missing
absorbance_43numeric216 unique values
0 missing
absorbance_44numeric216 unique values
0 missing
absorbance_45numeric216 unique values
0 missing
absorbance_46numeric216 unique values
0 missing
absorbance_47numeric216 unique values
0 missing
absorbance_48numeric216 unique values
0 missing
absorbance_49numeric216 unique values
0 missing
absorbance_50numeric216 unique values
0 missing
absorbance_51numeric216 unique values
0 missing
absorbance_52numeric216 unique values
0 missing
absorbance_53numeric216 unique values
0 missing
absorbance_54numeric216 unique values
0 missing
absorbance_55numeric215 unique values
0 missing
absorbance_56numeric215 unique values
0 missing
absorbance_57numeric215 unique values
0 missing
absorbance_58numeric216 unique values
0 missing
absorbance_59numeric215 unique values
0 missing
absorbance_60numeric216 unique values
0 missing
absorbance_61numeric216 unique values
0 missing
absorbance_62numeric216 unique values
0 missing
absorbance_63numeric216 unique values
0 missing
absorbance_64numeric216 unique values
0 missing
absorbance_65numeric216 unique values
0 missing
absorbance_66numeric216 unique values
0 missing
absorbance_67numeric216 unique values
0 missing
absorbance_68numeric215 unique values
0 missing
absorbance_69numeric216 unique values
0 missing
absorbance_70numeric216 unique values
0 missing
absorbance_71numeric216 unique values
0 missing
absorbance_72numeric216 unique values
0 missing
absorbance_73numeric216 unique values
0 missing
absorbance_74numeric216 unique values
0 missing
absorbance_75numeric216 unique values
0 missing
absorbance_76numeric216 unique values
0 missing
absorbance_77numeric216 unique values
0 missing
absorbance_78numeric216 unique values
0 missing
absorbance_79numeric216 unique values
0 missing
absorbance_80numeric216 unique values
0 missing
absorbance_81numeric215 unique values
0 missing
absorbance_82numeric216 unique values
0 missing
absorbance_83numeric216 unique values
0 missing
absorbance_84numeric216 unique values
0 missing
absorbance_85numeric216 unique values
0 missing
absorbance_86numeric215 unique values
0 missing
absorbance_87numeric216 unique values
0 missing
absorbance_88numeric216 unique values
0 missing
absorbance_89numeric216 unique values
0 missing
absorbance_90numeric216 unique values
0 missing
absorbance_91numeric216 unique values
0 missing
absorbance_92numeric216 unique values
0 missing
absorbance_93numeric215 unique values
0 missing
absorbance_94numeric214 unique values
0 missing
absorbance_95numeric216 unique values
0 missing
absorbance_96numeric216 unique values
0 missing
absorbance_97numeric216 unique values
0 missing
absorbance_98numeric216 unique values
0 missing
absorbance_99numeric216 unique values
0 missing
absorbance_100numeric216 unique values
0 missing
principal_component_1numeric216 unique values
0 missing
principal_component_2numeric216 unique values
0 missing
principal_component_3numeric216 unique values
0 missing
principal_component_4numeric217 unique values
0 missing
principal_component_5numeric216 unique values
0 missing
principal_component_6numeric216 unique values
0 missing
principal_component_7numeric216 unique values
0 missing
principal_component_8numeric216 unique values
0 missing
principal_component_9numeric216 unique values
0 missing
principal_component_10numeric216 unique values
0 missing
principal_component_11numeric216 unique values
0 missing
principal_component_12numeric216 unique values
0 missing
principal_component_13numeric216 unique values
0 missing
principal_component_14numeric217 unique values
0 missing
principal_component_15numeric216 unique values
0 missing
principal_component_16numeric216 unique values
0 missing
principal_component_17numeric216 unique values
0 missing
principal_component_18numeric217 unique values
0 missing
principal_component_19numeric216 unique values
0 missing
principal_component_20numeric216 unique values
0 missing
principal_component_21numeric216 unique values
0 missing
principal_component_22numeric216 unique values
0 missing
moisturenumeric141 unique values
0 missing
fatnumeric157 unique values
0 missing

107 properties

240
Number of instances (rows) of the dataset.
125
Number of attributes (columns) of the dataset.
2
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.
124
Number of numeric attributes.
1
Number of nominal attributes.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.91
Third quartile of skewness among attributes of the numeric type.
0.67
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
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.03
Mean kurtosis among attributes of the numeric type.
0.33
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.58
First quartile of kurtosis among attributes of the numeric type.
0.55
Third quartile of standard deviation of attributes of the numeric type.
0.87
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
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.15
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
3.24
Mean of means among attributes of the numeric type.
0.31
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.84
First quartile of means among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.16
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
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.67
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
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.85
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.
0.79
First quartile of skewness among attributes of the numeric type.
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.87
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.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
0.51
First quartile of standard deviation of attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.16
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
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.7
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.67
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
0.12
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
57.5
Percentage of instances belonging to the most frequent class.
0.8
Mean standard deviation of attributes of the numeric type.
0.66
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.98
Entropy of the target attribute values.
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
138
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
3.09
Second quartile (Median) of means among attributes of the numeric type.
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.02
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.15
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
9.36
Maximum kurtosis among attributes of the numeric type.
-0.15
Minimum of means among attributes of the numeric type.
0.83
Second quartile (Median) of skewness among attributes of the numeric type.
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.68
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
62.85
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.8
Percentage of binary attributes.
0.55
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.52
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.18
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.
2
The maximum number of distinct values among attributes of the nominal type.
-1.55
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
0.83
Third quartile of kurtosis among attributes of the numeric type.
0.79
Average class difference between consecutive instances.
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.69
Maximum skewness among attributes of the numeric type.
0.41
Minimum standard deviation of attributes of the numeric type.
99.2
Percentage of numeric attributes.
3.41
Third quartile of means among attributes of the numeric type.
0.87
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
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.15
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
14.36
Maximum standard deviation of attributes of the numeric type.
42.5
Percentage of instances belonging to the least frequent class.
0.8
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.16
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
0.18
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
102
Number of instances belonging to the least frequent class.

7 tasks

537 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
208 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: binaryClass
0 runs - estimation_procedure: 33% Holdout set - target_feature: binaryClass
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: binaryClass
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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