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labor

labor

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Author: Unknown Source: Collective Barganing Review, Labour Canada Please cite: https://archive.ics.uci.edu/ml/citation_policy.html Date: Tue, 15 Nov 88 15:44:08 EST From: stan To: aha@ICS.UCI.EDU 1. Title: Final settlements in labor negotitions in Canadian industry 2. Source Information -- Creators: Collective Barganing Review, montly publication, Labour Canada, Industrial Relations Information Service, Ottawa, Ontario, K1A 0J2, Canada, (819) 997-3117 The data includes all collective agreements reached in the business and personal services sector for locals with at least 500 members (teachers, nurses, university staff, police, etc) in Canada in 87 and first quarter of 88. -- Donor: Stan Matwin, Computer Science Dept, University of Ottawa, 34 Somerset East, K1N 9B4, (stan@uotcsi2.bitnet) -- Date: November 1988 3. Past Usage: -- testing concept learning software, in particular an experimental method to learn two-tiered concept descriptions. The data was used to learn the description of an acceptable and unacceptable contract. The unacceptable contracts were either obtained by interviewing experts, or by inventing near misses. Examples of use are described in: Bergadano, F., Matwin, S., Michalski, R., Zhang, J., Measuring Quality of Concept Descriptions, Procs. of the 3rd European Working Sessions on Learning, Glasgow, October 1988. Bergadano, F., Matwin, S., Michalski, R., Zhang, J., Representing and Acquiring Imprecise and Context-dependent Concepts in Knowledge-based Systems, Procs. of ISMIS'88, North Holland, 1988. 4. Relevant Information: -- data was used to test 2tier approach with learning from positive and negative examples 5. Number of Instances: 57 6. Number of Attributes: 16 7. Attribute Information: 1. dur: duration of agreement [1..7] 2 wage1.wage : wage increase in first year of contract [2.0 .. 7.0] 3 wage2.wage : wage increase in second year of contract [2.0 .. 7.0] 4 wage3.wage : wage increase in third year of contract [2.0 .. 7.0] 5 cola : cost of living allowance [none, tcf, tc] 6 hours.hrs : number of working hours during week [35 .. 40] 7 pension : employer contributions to pension plan [none, ret_allw, empl_contr] 8 stby_pay : standby pay [2 .. 25] 9 shift_diff : shift differencial : supplement for work on II and III shift [1 .. 25] 10 educ_allw.boolean : education allowance [true false] 11 holidays : number of statutory holidays [9 .. 15] 12 vacation : number of paid vacation days [ba, avg, gnr] 13 lngtrm_disabil.boolean : employer's help during employee longterm disabil ity [true , false] 14 dntl_ins : employers contribution towards the dental plan [none, half, full] 15 bereavement.boolean : employer's financial contribution towards the covering the costs of bereavement [true , false] 16 empl_hplan : employer's contribution towards the health plan [none, half, full] 8. Missing Attribute Values: None 9. Class Distribution: 10. Exceptions from format instructions: no commas between attribute values.

17 features

class (target)nominal2 unique values
0 missing
shift-differentialnumeric10 unique values
26 missing
contribution-to-health-plannominal3 unique values
20 missing
bereavement-assistancenominal2 unique values
27 missing
contribution-to-dental-plannominal3 unique values
20 missing
longterm-disability-assistancenominal2 unique values
29 missing
vacationnominal3 unique values
6 missing
statutory-holidaysnumeric6 unique values
4 missing
education-allowancenominal2 unique values
35 missing
durationnumeric3 unique values
1 missing
standby-paynumeric7 unique values
48 missing
pensionnominal3 unique values
30 missing
working-hoursnumeric8 unique values
6 missing
cost-of-living-adjustmentnominal3 unique values
20 missing
wage-increase-third-yearnumeric9 unique values
42 missing
wage-increase-second-yearnumeric15 unique values
11 missing
wage-increase-first-yearnumeric17 unique values
1 missing
Show all 17 features

107 properties

NumberOfInstances
57
Number of instances (rows) of the dataset.
NumberOfFeatures
17
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
326
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
56
Number of instances with at least one value missing.
NumberOfNumericFeatures
8
Number of numeric attributes.
NumberOfSymbolicFeatures
9
Number of nominal attributes.
AutoCorrelation
0.75
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.73
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
0.25
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
0.46
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
0.73
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
0.25
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
0.46
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
0.73
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
0.25
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
0.46
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
0.93
Entropy of the target attribute values.
DecisionStumpAUC
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.32
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.32
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.3
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
9.29
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
64.91
Percentage of instances belonging to the most frequent class.
MajorityClassSize
37
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
1.56
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
13.19
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
38.04
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.24
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
3.32
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
5.03
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.04
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
2.05
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
9.41
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.1
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
9.32
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
2.56
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.26
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
2.24
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
0.3
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-2.04
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
2.16
Minimum of means among attributes of the numeric type.
MinMutualInformation
0.01
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-2.01
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.71
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
35.09
Percentage of instances belonging to the least frequent class.
MinorityClassSize
20
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.07
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
4
Number of binary attributes.
PercentageOfBinaryFeatures
23.53
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
98.25
Percentage of instances having missing values.
PercentageOfMissingValues
33.64
Percentage of missing values.
PercentageOfNumericFeatures
47.06
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
52.94
Percentage of nominal attributes.
Quartile1AttributeEntropy
0.62
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-1.41
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
3.83
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.03
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.56
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
1.19
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1.09
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.26
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
4.42
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.08
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.16
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1.34
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
1.48
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
5.18
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
10.18
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.16
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.81
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
4.03
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.3
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.34
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0.53
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.56
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

29 tasks

Supervised Classification on labor
6416 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on labor
355 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on labor
336 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on labor
220 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
Supervised Classification on labor
31 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
Supervised Classification on labor
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on labor
218 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on labor
81 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
Learning Curve on labor
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on labor
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on labor
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on labor
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on labor
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on labor
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on labor
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on labor
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
Learning Curve on labor
0 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: jaccard - target_feature: class
Supervised Data Stream Classification on labor
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
Clustering on labor
0 runs
Clustering on labor
0 runs - estimation_procedure: 50 times Clustering
Clustering on labor
0 runs - estimation_procedure: 50 times Clustering
Clustering on labor
0 runs - estimation_procedure: 50 times Clustering
Clustering on labor
0 runs - estimation_procedure: 50 times Clustering
Clustering on labor
0 runs - estimation_procedure: 50 times Clustering
Clustering on labor
0 runs - estimation_procedure: 50 times Clustering
Clustering on labor
0 runs - estimation_procedure: 50 times Clustering
Clustering on labor
0 runs - estimation_procedure: 50 times Clustering
Clustering on labor
0 runs - estimation_procedure: 50 times Clustering
Clustering on labor
0 runs - estimation_procedure: 50 times Clustering
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