Use case on the amazon-employee-access dataset

Dataset Amazon_employee_access

Machine Learning Task: Binary classification

This is an Amazon_employee_access database. 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 employees' unseen set. 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.

Available at OpenML: https://openml.org/d/4135

Category: Technology

# Rows: 32,769 # Columns: 9

Target: target

Features

Nominal: RESOURCE, MGR_ID, ROLE_ROLLUP_1, ROLE_ROLLUP_2, ROLE_DEPTNAME, ROLE_TITLE, ROLE_FAMILY_DESC, ROLE_FAMILY, ROLE_CODE

Machine Learning Use Case Technology

Area Under ROC Curve (AUC)

Amazon Employee Access Auc

Accuracy (ACC)

Amazon Employee Access Acc

Balanced Accuracy (BALACC)

Amazon Employee Access Balacc

Cross-Entropy Loss (LOGLOSS)

Amazon Employee Access Logloss

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