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Amazon_employee_access

Amazon_employee_access

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Author: Source: [Kaggle Amazon Employee Access Challenge](https://www.kaggle.com/c/amazon-employee-access-challenge) Please cite: ### Description The data consists of real historical data collected from 2010 & 2011. Employees are manually allowed or denied access to resources over time. The data is used to create an algorithm capable of learning from this historical data to predict approval/denial for an unseen set of employees. ### Dataset Information When an employee at any company starts work, they first need to obtain the computer access necessary to fulfill their role. This access may allow an employee to read/manipulate resources through various applications or web portals. It is assumed that employees fulfilling the functions of a given role will access the same or similar resources. It is often the case that employees figure out the access they need as they encounter roadblocks during their daily work (e.g. not able to log into a reporting portal). A knowledgeable supervisor then takes time to manually grant the needed access in order to overcome access obstacles. As employees move throughout a company, this access discovery/recovery cycle wastes a non-trivial amount of time and money. There is a considerable amount of data regarding an employee’s role within an organization and the resources to which they have access. Given the data related to current employees and their provisioned access, models can be built that automatically determine access privileges as employees enter and leave roles within a company. These auto-access models seek to minimize the human involvement required to grant or revoke employee access. The original training and test set were merged. ### Attributes Information * ACTION [target]: ACTION is 1 if the resource was approved, 0 if the resource was not * RESOURCE: An ID for each resource * MGR_ID: The EMPLOYEE ID of the manager of the current EMPLOYEE ID record; an employee may have only one manager at a time * ROLE_ROLLUP_1: Company role grouping category id 1 (e.g. US Engineering) * ROLE_ROLLUP_2: Company role grouping category id 2 (e.g. US Retail) * ROLE_DEPTNAME: Company role department description (e.g. Retail) * ROLE_TITLE: Company role business title description (e.g. Senior Engineering Retail Manager) * ROLE_FAMILY_DESC: Company role family extended description (e.g. Retail Manager, Software Engineering) * ROLE_FAMILY: Company role family description (e.g. Retail Manager) * ROLE_CODE: Company role code; this code is unique to each role (e.g. Manager)

10 features

target (target)nominal2 unique values
0 missing
RESOURCEnominal7518 unique values
0 missing
MGR_IDnominal4243 unique values
0 missing
ROLE_ROLLUP_1nominal128 unique values
0 missing
ROLE_ROLLUP_2nominal177 unique values
0 missing
ROLE_DEPTNAMEnominal449 unique values
0 missing
ROLE_TITLEnominal343 unique values
0 missing
ROLE_FAMILY_DESCnominal2358 unique values
0 missing
ROLE_FAMILYnominal67 unique values
0 missing
ROLE_CODEnominal343 unique values
0 missing
Show all 10 features

107 properties

NumberOfInstances
32769
Number of instances (rows) of the dataset.
NumberOfFeatures
10
Number of attributes (columns) of the dataset.
NumberOfClasses
2
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
0
Number of numeric attributes.
NumberOfSymbolicFeatures
10
Number of nominal attributes.
AutoCorrelation
0.89
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.51
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
0.06
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
0.01
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
0.75
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
0.06
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
0.08
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
0.79
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
0.06
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
0.17
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
0.32
Entropy of the target attribute values.
DecisionStumpAUC
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
5.72
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
94.21
Percentage of instances belonging to the most frequent class.
MajorityClassSize
30872
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
11.26
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0.15
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
7518
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
6.91
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
Mean of means among attributes of the numeric type.
MeanMutualInformation
0.06
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
122.8
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
1562.8
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
3
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
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
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
5.79
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1897
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.11
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
1
Number of binary attributes.
PercentageOfBinaryFeatures
10
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
0
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
100
Percentage of nominal attributes.
Quartile1AttributeEntropy
4.56
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
6.09
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
9.45
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0.1
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.06
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
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.06
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
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.06
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
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.29
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
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
2497.76
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

31 tasks

Supervised Classification on Amazon_employee_access
22125 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: target
Supervised Classification on Amazon_employee_access
10955 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: target
Supervised Classification on Amazon_employee_access
2 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: target
Supervised Classification on Amazon_employee_access
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: target
Supervised Classification on Amazon_employee_access
0 runs - estimation_procedure: 10% Holdout set - evaluation_measure: predictive_accuracy - target_feature: ROLE_TITLE
Supervised Classification on Amazon_employee_access
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: target
Supervised Classification on Amazon_employee_access
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: target
Supervised Classification on Amazon_employee_access
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: target
Supervised Classification on Amazon_employee_access
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: f1 - target_feature: target
Supervised Classification on Amazon_employee_access
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: target
Learning Curve on Amazon_employee_access
40 runs - estimation_procedure: 10-fold Learning Curve - target_feature: target
Clustering on Amazon_employee_access
0 runs - target_feature: target
Clustering on Amazon_employee_access
0 runs
Clustering on Amazon_employee_access
0 runs - estimation_procedure: 50 times Clustering
Clustering on Amazon_employee_access
0 runs - estimation_procedure: 50 times Clustering
Clustering on Amazon_employee_access
0 runs - estimation_procedure: 50 times Clustering
Clustering on Amazon_employee_access
0 runs - estimation_procedure: 50 times Clustering
Clustering on Amazon_employee_access
0 runs - estimation_procedure: 50 times Clustering
Clustering on Amazon_employee_access
0 runs - estimation_procedure: 50 times Clustering
Clustering on Amazon_employee_access
0 runs - estimation_procedure: 50 times Clustering
Clustering on Amazon_employee_access
0 runs - estimation_procedure: 50 times Clustering
Clustering on Amazon_employee_access
0 runs - estimation_procedure: 50 times Clustering
Clustering on Amazon_employee_access
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on Amazon_employee_access
1300 runs - target_feature: target
Subgroup Discovery on Amazon_employee_access
1298 runs - target_feature: target
Subgroup Discovery on Amazon_employee_access
0 runs - target_feature: target
Subgroup Discovery on Amazon_employee_access
0 runs - target_feature: target
Subgroup Discovery on Amazon_employee_access
0 runs - target_feature: target
Subgroup Discovery on Amazon_employee_access
0 runs - target_feature: target
Subgroup Discovery on Amazon_employee_access
0 runs - target_feature: target
Subgroup Discovery on Amazon_employee_access
0 runs - target_feature: target
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