Data
Amazon_employee_access

Amazon_employee_access

active ARFF Publicly available Visibility: public Uploaded 05-11-2015 by Joaquin Vanschoren
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  • OpenML100 study_123 study_14 study_7
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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

107 properties

32769
Number of instances (rows) of the dataset.
10
Number of attributes (columns) of the dataset.
2
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.
0
Number of numeric attributes.
10
Number of nominal attributes.
122.8
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
1562.8
Average number of distinct values among the attributes of the nominal type.
First quartile of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
2497.76
Standard deviation of the number of distinct values among attributes of the nominal type.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
Mean standard deviation of attributes of the numeric type.
6.09
Second quartile (Median) of entropy among attributes.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
94.21
Percentage of instances belonging to the most frequent class.
3
Minimal entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.32
Entropy of the target attribute values.
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
30872
Number of instances belonging to the most frequent class.
Minimum kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
11.26
Maximum entropy among attributes.
Minimum of means among attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.06
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum kurtosis among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
10
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
0.15
Maximum mutual information between the nominal attributes and the target attribute.
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
9.45
Third quartile of entropy among attributes.
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
5.72
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
7518
The maximum number of distinct values among attributes of the nominal type.
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
0.89
Average class difference between consecutive instances.
0.11
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum skewness among attributes of the numeric type.
5.79
Percentage of instances belonging to the least frequent class.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
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
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Maximum standard deviation of attributes of the numeric type.
1897
Number of instances belonging to the least frequent class.
100
Percentage of nominal attributes.
0.1
Third quartile of mutual information between the nominal attributes and the target attribute.
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
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
6.91
Average entropy of the attributes.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
4.56
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
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
0.29
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean kurtosis among attributes of the numeric type.
0.11
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
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
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Mean of means among attributes of the numeric type.
0.06
Average mutual information between the nominal attributes and the target attribute.
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of means among attributes of the numeric type.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

15 tasks

18850 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: target
10953 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: target
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: target
40 runs - estimation_procedure: 10-fold Learning Curve - target_feature: target
0 runs - target_feature: target
1300 runs - target_feature: target
1298 runs - target_feature: target
0 runs - target_feature: target
0 runs - target_feature: target
0 runs - target_feature: target
0 runs - target_feature: target
0 runs - target_feature: target
0 runs - target_feature: target
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