Data
Amazon_employee_access

Amazon_employee_access

active ARFF Publicly available Visibility: public Uploaded 05-11-2015 by Joaquin Vanschoren
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Data from the Kaggle Amazon Employee Access Challenge: https://www.kaggle.com/c/amazon-employee-access-challenge 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 nontrivial 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.

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.
0.89
Average class difference between consecutive instances.
0.5
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
0.06
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
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
0.5
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
0.06
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
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
0.5
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
0.06
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
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
0.32
Entropy of the target attribute values.
0.52
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.06
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Number of attributes divided by the number of instances.
5.72
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.06
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
94.21
Percentage of instances belonging to the most frequent class.
30872
Number of instances belonging to the most frequent class.
11.26
Maximum entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
0.15
Maximum mutual information between the nominal attributes and the target attribute.
7518
The maximum number of distinct values among attributes of the nominal type.
Maximum skewness among attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
6.91
Average entropy of the attributes.
Mean kurtosis among attributes of the numeric type.
Mean of means among attributes of the numeric type.
0.06
Average mutual information between the nominal attributes and the target attribute.
122.8
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1562.8
Average number of distinct values among the attributes of the nominal type.
Mean skewness among attributes of the numeric type.
Mean standard deviation of attributes of the numeric type.
3
Minimal entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0.01
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
5.79
Percentage of instances belonging to the least frequent class.
1897
Number of instances belonging to the least frequent class.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.11
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.19
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1
Number of binary attributes.
10
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0
Percentage of numeric attributes.
100
Percentage of nominal attributes.
4.56
First quartile of entropy among attributes.
First quartile of kurtosis among attributes of the numeric type.
First quartile of means among attributes of the numeric type.
0.02
First quartile of mutual information between the nominal attributes and the target attribute.
First quartile of skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
6.09
Second quartile (Median) of entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.03
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
Second quartile (Median) of standard deviation of attributes of the numeric type.
9.45
Third quartile of entropy among attributes.
Third quartile of kurtosis among attributes of the numeric type.
Third quartile of means among attributes of the numeric type.
0.1
Third quartile of mutual information between the nominal attributes and the target attribute.
Third quartile of skewness among attributes of the numeric type.
Third 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 1
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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.trees.REPTree -L 2
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.5
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.06
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.06
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
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.RandomTree -depth 2
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.77
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.RandomTree -depth 3
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
2497.76
Standard deviation of the number of distinct values among attributes of the nominal type.
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.06
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.44
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

12 tasks

0 runs - estimation_procedure: Test on Training Data - target_feature: target
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: target
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: target
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: target
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: target
0 runs - estimation_procedure: Leave one out - target_feature: target
0 runs - estimation_procedure: 10% Holdout set - target_feature: target
0 runs - estimation_procedure: 33% Holdout set - target_feature: target
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: target
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: target
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: MGR_ID
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: target
Define a new task