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desharnais

desharnais

deactivated ARFF Publicly available Visibility: public Uploaded 06-10-2014 by Joaquin Vanschoren
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Author: Source: Unknown - Date unknown Please cite: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This is a PROMISE Software Engineering Repository data set made publicly available in order to encourage repeatable, verifiable, refutable, and/or improvable predictive models of software engineering. If you publish material based on PROMISE data sets then, please follow the acknowledgment guidelines posted on the PROMISE repository web page http://promise.site.uottawa.ca/SERepository . %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Domain: software cost estimation Donor: Martin Shepperd Corrections by: Dan Port with information from Barbara Kitchenham Notes: 4 incomplete projects (projects 38,44,66,75) so often people use the 77 complete cases. The original source is [1]. Our paper [2] provides a basic overview of the data set. References: [1] J. M. Desharnais, "Analyse statistique de la productivitie des projets informatique a partie de la technique des point des fonction," University of Montreal, Masters Thesis, 1989. [2] M. J. Shepperd and C. Schofield, "Estimating software project effort using analogies," IEEE Transactions on Software Engineering, vol. 23, pp. 736-743, 1997. [3] Dreger, J. Brian. "Function Point Analysis" Englewood Cliffs, NJ: Prentice Hall, 1989. Attributes: Note: Many of the above attributes are numerically coded catagorical variables. Values of -1 indicate missing data, Sample run (WEKA): === Run information === Scheme: weka.classifiers.functions.LinearRegression -S 0 -R 1.0E-8 Relation: martin.csv Instances: 81 Attributes: 12 Project TeamExp ManagerExp YearEnd Length Effort Transactions Entities PointsNonAdjust Adjustment PointsAjust Langage Note from dport: the regression model below is problematic in that it treats catagorical variables as continuous values and it includes the dependent variable Length as an independent variable which is likely a mistake. Test mode: 10-fold cross-validation === Classifier model (full training set) === Linear Regression Model Effort = -433.25 * TeamExp + 408.8057 * ManagerExp + 201.2701 * Length + 4.2361 * Transactions + 7.9056 * Entities + 4.4594 * PointsAdjust + 92.1389 * Adjustment + -1777.4579 * Langage + -278.0786 Time taken to build model: 0.02 seconds === Cross-validation === === Summary === Correlation coefficient 0.7303 Mean absolute error 2082.1182 Root mean squared error 3007.0504 Relative absolute error 64.9965 % Root relative squared error 67.3788 % Total Number of Instances 81

12 features

Language (target)nominal3 unique values
0 missing
Projectnumeric81 unique values
0 missing
TeamExpnumeric6 unique values
0 missing
ManagerExpnumeric8 unique values
0 missing
YearEndnumeric7 unique values
0 missing
Lengthnumeric26 unique values
0 missing
Effortnumeric81 unique values
0 missing
Transactionsnumeric73 unique values
0 missing
Entitiesnumeric61 unique values
0 missing
PointsNonAdjustnumeric77 unique values
0 missing
Adjustmentnumeric34 unique values
0 missing
PointsAjustnumeric77 unique values
0 missing

107 properties

81
Number of instances (rows) of the dataset.
12
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.
11
Number of numeric attributes.
1
Number of nominal attributes.
1.48
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.36
Entropy of the target attribute values.
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
46
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
85.74
Second quartile (Median) of means among attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.59
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.47
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
7.38
Maximum kurtosis among attributes of the numeric type.
2.19
Minimum of means among attributes of the numeric type.
1.34
Second quartile (Median) of skewness among attributes of the numeric type.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.15
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
5046.31
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
23.53
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.15
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 instances having missing values.
Third quartile of entropy among attributes.
0.44
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.
-0.45
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
4.72
Third quartile of kurtosis among attributes of the numeric type.
0.63
Average class difference between consecutive instances.
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.29
Maximum skewness among attributes of the numeric type.
1.22
Minimum standard deviation of attributes of the numeric type.
91.67
Percentage of numeric attributes.
289.23
Third quartile of means among attributes of the numeric type.
0.57
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
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
4418.77
Maximum standard deviation of attributes of the numeric type.
12.35
Percentage of instances belonging to the least frequent class.
8.33
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.48
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
0.44
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
10
Number of instances belonging to the least frequent class.
First quartile of entropy among attributes.
1.73
Third quartile of skewness among attributes of the numeric type.
0.12
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
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.13
Mean kurtosis among attributes of the numeric type.
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.47
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.28
First quartile of kurtosis among attributes of the numeric type.
180.21
Third quartile of standard deviation of attributes of the numeric type.
0.57
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
0.61
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.33
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
555.93
Mean of means among attributes of the numeric type.
0.13
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
11.67
First quartile of means among attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.48
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
0.44
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.12
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
0.21
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.68
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.
-0.11
First quartile of skewness among attributes of the numeric type.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.57
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.
0.33
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.
1.64
First quartile of standard deviation of attributes of the numeric type.
0.62
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.48
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
0.63
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.89
Mean skewness among attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.41
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.12
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
0.44
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
56.79
Percentage of instances belonging to the most frequent class.
459.95
Mean standard deviation of attributes of the numeric type.

14 tasks

362 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Language
180 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Language
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Language
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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