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squash-stored

squash-stored

active ARFF Publicly available Visibility: public Uploaded 26-08-2014 by Felicia West
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Author: Winna Harvey Source: [original](http://www.cs.waikato.ac.nz/ml/weka/datasets.html) - Please cite: Squash Harvest Stored Data source: Winna Harvey Crop and Food Research, Christchurch, New Zealand The purpose of the research was to determine the changes taking place in squash fruit during the maturation and ripening so as to pinpoint the best time to give the best quality at the marketplace (Japan). The squash is transported to Japan by refrigerated cargo vessels and takes three to four weeks to reach the market. Evaluations were carried out at a stage representing the quality inspection stage prior to export and also at the stage it would reach on arriving at the market place. The original objectives were to determine which pre-harvest variables contribute to good tasting squash after different periods of storage time. This is determined by whether a measure of acceptability found by categorising each squash as either unacceptable, acceptable or excellent. The fruit in this dataset were stored before being measured, and they have an extra attribute that squash-unstored lacks - the weight of the fruit after storage. Attribute Information: 1. site - where fruit is located - enumerated 2. daf - number of days after flowering - enumerated 3. fruit - individual number of the fruit (not unique) - enumerated 4. weight - weight of whole fruit in grams - real 5. storewt - weight of fruit after storage - real 6. pene - penetrometer indicates maturity of fruit at harvest - integer 7. solids_% - a test for dry matter - integer 8. brix - a refractometer measurement used to indicate sweetness or ripeness of the fruit - integer 9. a - the a coordinate of the HunterLab L-a-b notation of colour measurement - integer 10. egdd - the heat accumulation above a base of 8c from emergence of the plant to harvest of the fruit - real 11. fgdd - the heat accumulation above a base of 8c from flowering to harvesting - real 12. groundspot_a - the number indicating colour of skin where the fruit rested on the ground - integer 13. glucose - measured in mg/100g of fresh weight - integer 14. fructose - measured in mg/100g of fresh weight - integer 15. sucrose - measured in mg/100g of fresh weight - integer 16. total - measured in mg/100g of fresh weight - integer 17. glucose+fructose - measured in mg/100g of fresh weight - integer 18. starch - measured in mg/100g of fresh weight - integer 19. sweetness - the mean of eight taste panel scores; out of 1500 - integer 20. flavour - the mean of eight taste panel scores; out of 1500 - integer 21. dry/moist - the mean of eight taste panel scores; out of 1500 - integer 22. fibre - the mean of eight taste panel scores; out of 1500 - integer 23. heat_input_emerg - the amount of heat emergence after harvest - real 24. heat_input_flower - the amount of heat input before flowering - real 25. Acceptability - the acceptability of the fruit - enumerated

25 features

Acceptability (target)nominal3 unique values
0 missing
glucosenumeric51 unique values
1 missing
heat_input_flowernumeric14 unique values
0 missing
heat_input_emergnumeric14 unique values
0 missing
fibrenumeric52 unique values
0 missing
dry/moistnumeric51 unique values
0 missing
flavournumeric52 unique values
0 missing
sweetnessnumeric51 unique values
0 missing
starchnumeric51 unique values
1 missing
glucose+fructosenumeric50 unique values
1 missing
totalnumeric51 unique values
1 missing
sucrosenumeric51 unique values
1 missing
fructosenumeric50 unique values
1 missing
sitenominal3 unique values
0 missing
groundspot_a*numeric51 unique values
1 missing
fgddnumeric13 unique values
0 missing
egddnumeric13 unique values
0 missing
a*numeric45 unique values
0 missing
brixnumeric31 unique values
0 missing
solidsnumeric43 unique values
0 missing
penenumeric37 unique values
0 missing
storewtnumeric52 unique values
0 missing
weightnumeric50 unique values
0 missing
fruitnominal22 unique values
0 missing
dafnominal5 unique values
0 missing
Show all 25 features

107 properties

NumberOfInstances
52
Number of instances (rows) of the dataset.
NumberOfFeatures
25
Number of attributes (columns) of the dataset.
NumberOfClasses
3
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
7
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
2
Number of instances with at least one value missing.
NumberOfNumericFeatures
21
Number of numeric attributes.
NumberOfSymbolicFeatures
4
Number of nominal attributes.
AutoCorrelation
0.51
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.7
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
0.4
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
0.31
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
0.7
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
0.4
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
0.31
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
0.7
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
0.4
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
0.31
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
1.46
Entropy of the target attribute values.
DecisionStumpAUC
0.69
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.38
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.48
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
3.89
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.37
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.37
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
44.23
Percentage of instances belonging to the most frequent class.
MajorityClassSize
23
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
4.24
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1.24
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1859.48
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.63
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
22
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.97
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
425.91
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
2.69
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.31
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
412.46
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.38
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
6.13
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
8.25
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
-0.02
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
94.86
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
1.58
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.05
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
8.03
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.24
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
-1.17
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
1.84
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
15.38
Percentage of instances belonging to the least frequent class.
MinorityClassSize
8
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.38
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.38
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
3.85
Percentage of instances having missing values.
PercentageOfMissingValues
0.54
Percentage of missing values.
PercentageOfNumericFeatures
84
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
16
Percentage of nominal attributes.
Quartile1AttributeEntropy
1.58
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.71
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
17.29
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.24
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.52
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
4.67
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
2.24
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.37
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
89.25
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.26
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
35
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
4.24
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-0.07
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
703.19
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.63
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.54
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
156.19
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.56
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.56
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.56
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.48
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.48
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.48
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
9.22
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.64
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.44
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Classification on squash-stored
380 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Acceptability
Supervised Classification on squash-stored
309 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Acceptability
Supervised Classification on squash-stored
178 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Acceptability
Supervised Data Stream Classification on squash-stored
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Acceptability
Clustering on squash-stored
0 runs
Clustering on squash-stored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-stored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-stored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-stored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-stored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-stored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-stored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-stored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-stored
0 runs - estimation_procedure: 50 times Clustering
Clustering on squash-stored
0 runs - estimation_procedure: 50 times Clustering
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