Data
squash-unstored

squash-unstored

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Author: Winna Harvey Source: [original](http://www.cs.waikato.ac.nz/ml/weka/datasets.html) - Please cite: Squash Harvest Unstored 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 market place (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 not stored before being measured, so they lack an attribute present in the stored data - 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. pene - penetrometer indicates maturity of fruit at harvest - integer 6. solids_% - a test for dry matter - integer 7. brix - a refractometer measurement used to indicate sweetness or ripeness of the fruit - integer 8. a - the a-coordinate of the HunterLab L-a-b notation of colour measurement - integer 9. egdd - the heat accumulation above a base of 8c from emergence of the plant to harvest of the fruit - real 10. fgdd - the heat accumulation above a base of 8c from flowering to harvesting - real 11. groundspot_a - the number indicating colour of skin where the fruit rested on the ground - integer 12. glucose - measured in mg/100g of fresh weight - integer 13. fructose - measured in mg/100g of fresh weight - integer 14. sucrose - measured in mg/100g of fresh weight - integer 15. total - measured in mg/100g of fresh weight - integer 16. glucose+fructose - measured in mg/100g of fresh weight - integer 17. starch - measured in mg/100g of fresh weight - integer 18. sweetness - the mean of eight taste panel scores; out of 1500 - integer 19. flavour - the mean of eight taste panel scores; out of 1500 - integer 20. dry/moist - the mean of eight taste panel scores; out of 1500 - integer 21. fibre - the mean of eight taste panel scores; out of 1500 - integer 22. heat_input_emerg - the amount of heat emergence after harvest - real 23. heat_input_flower - the amount of heat input before flowering - real 24. Acceptability - the acceptability of the fruit - enumerated

24 features

Acceptability (target)nominal3 unique values
0 missing
sitenominal3 unique values
0 missing
dafnominal5 unique values
0 missing
fruitnominal22 unique values
0 missing
weightnumeric49 unique values
0 missing
penenumeric36 unique values
0 missing
solidsnumeric46 unique values
0 missing
brixnumeric36 unique values
0 missing
a*numeric43 unique values
0 missing
egddnumeric13 unique values
0 missing
fgddnumeric13 unique values
0 missing
groundspot_a*numeric48 unique values
2 missing
glucosenumeric46 unique values
6 missing
fructosenumeric45 unique values
6 missing
sucrosenumeric46 unique values
6 missing
totalnumeric46 unique values
6 missing
glucose+fructosenumeric46 unique values
6 missing
starchnumeric46 unique values
6 missing
sweetnessnumeric52 unique values
0 missing
flavournumeric51 unique values
0 missing
dry/moistnumeric52 unique values
0 missing
fibrenumeric50 unique values
1 missing
heat_input_emergnumeric13 unique values
0 missing
heat_input_flowernumeric13 unique values
0 missing

107 properties

52
Number of instances (rows) of the dataset.
24
Number of attributes (columns) of the dataset.
3
Number of distinct values of the target attribute (if it is nominal).
39
Number of missing values in the dataset.
9
Number of instances with at least one value missing.
20
Number of numeric attributes.
4
Number of nominal attributes.
7.26
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.
0.15
First quartile of mutual information between the nominal attributes and the target attribute.
0.42
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.42
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
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
8.25
Average number of distinct values among the attributes of the nominal type.
-0.11
First quartile of skewness among attributes of the numeric type.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.74
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
9.22
Standard deviation of the number of distinct values among attributes of the nominal type.
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.26
Mean skewness among attributes of the numeric type.
4.87
First quartile of standard deviation of attributes of the numeric type.
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.33
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
0.66
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
68.58
Mean standard deviation of attributes of the numeric type.
2.24
Second quartile (Median) of entropy among attributes.
0.42
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.42
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
0.38
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
46.15
Percentage of instances belonging to the most frequent class.
1.58
Minimal entropy among attributes.
-0.28
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.31
Entropy of the target attribute values.
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
24
Number of instances belonging to the most frequent class.
-0.83
Minimum kurtosis among attributes of the numeric type.
85.14
Second quartile (Median) of means among attributes of the numeric type.
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
4.26
Maximum entropy among attributes.
4.79
Minimum of means among attributes of the numeric type.
0.18
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.42
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.37
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.07
Maximum kurtosis among attributes of the numeric type.
0.15
Minimal mutual information between the nominal attributes and the target attribute.
0.33
Second quartile (Median) of skewness among attributes of the numeric type.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1735.98
Maximum of means among attributes of the numeric type.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
28.46
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.46
Number of attributes divided by the number of instances.
0.64
Maximum mutual information between the nominal attributes and the target attribute.
-0.79
Minimum skewness among attributes of the numeric type.
17.31
Percentage of instances having missing values.
4.26
Third quartile of entropy among attributes.
0.42
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
4.03
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
22
The maximum number of distinct values among attributes of the nominal type.
2.06
Minimum standard deviation of attributes of the numeric type.
3.13
Percentage of missing values.
-0.08
Third quartile of kurtosis among attributes of the numeric type.
0.47
Average class difference between consecutive instances.
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.92
Maximum skewness among attributes of the numeric type.
7.69
Percentage of instances belonging to the least frequent class.
83.33
Percentage of numeric attributes.
467.2
Third quartile of means among attributes of the numeric type.
0.74
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
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
325.27
Maximum standard deviation of attributes of the numeric type.
4
Number of instances belonging to the least frequent class.
16.67
Percentage of nominal attributes.
0.64
Third quartile of mutual information between the nominal attributes and the target attribute.
0.33
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
0.42
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.69
Average entropy of the attributes.
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.58
First quartile of entropy among attributes.
0.51
Third quartile of skewness among attributes of the numeric type.
0.42
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
0.26
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.22
Mean kurtosis among attributes of the numeric type.
0.31
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.5
First quartile of kurtosis among attributes of the numeric type.
105.29
Third quartile of standard deviation of attributes of the numeric type.
0.74
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
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.29
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
303.24
Mean of means among attributes of the numeric type.
0.33
Average mutual information between the nominal attributes and the target attribute.
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
9.99
First quartile of means among attributes of the numeric type.
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.33
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
0.42
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

7 tasks

383 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Acceptability
306 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Acceptability
187 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Acceptability
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Acceptability
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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