Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering
Fuente:
arXiv
Saved in:
| Main Author: | Vijayan, Naveen Edapurath |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Data vs. Model Machine Learning Fairness Testing: An Empirical Study
by: Shome, Arumoy, et al.
Published: (2024)
by: Shome, Arumoy, et al.
Published: (2024)
FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software
by: Yu, Normen, et al.
Published: (2024)
by: Yu, Normen, et al.
Published: (2024)
Maturity Framework for Enhancing Machine Learning Quality
by: Castelli, Angelantonio, et al.
Published: (2025)
by: Castelli, Angelantonio, et al.
Published: (2025)
Risk Management for Mitigating Benchmark Failure Modes: BenchRisk
by: McGregor, Sean, et al.
Published: (2025)
by: McGregor, Sean, et al.
Published: (2025)
Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review
by: Tulili, Tien Rahayu, et al.
Published: (2026)
by: Tulili, Tien Rahayu, et al.
Published: (2026)
Choosing the Right Path for AI Integration in Engineering Companies: A Strategic Guide
by: Dzhusupova, Rimma, et al.
Published: (2023)
by: Dzhusupova, Rimma, et al.
Published: (2023)
A Conceptual Framework for Ethical Evaluation of Machine Learning Systems
by: Gupta, Neha R., et al.
Published: (2024)
by: Gupta, Neha R., et al.
Published: (2024)
Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends
by: Taraghi, Mina, et al.
Published: (2024)
by: Taraghi, Mina, et al.
Published: (2024)
Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems
by: Du, Chengwen, et al.
Published: (2024)
by: Du, Chengwen, et al.
Published: (2024)
Federated Data Analytics for Cancer Immunotherapy: A Privacy-Preserving Collaborative Platform for Patient Management
by: Raheem, Mira, et al.
Published: (2025)
by: Raheem, Mira, et al.
Published: (2025)
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
by: Seedat, Nabeel, et al.
Published: (2022)
by: Seedat, Nabeel, et al.
Published: (2022)
Assessing the Use of AutoML for Data-Driven Software Engineering
by: Calefato, Fabio, et al.
Published: (2023)
by: Calefato, Fabio, et al.
Published: (2023)
An Effective Software Risk Prediction Management Analysis of Data Using Machine Learning and Data Mining Method
by: Xu, Jinxin, et al.
Published: (2024)
by: Xu, Jinxin, et al.
Published: (2024)
Data Virtualization for Machine Learning
by: Khan, Saiful, et al.
Published: (2025)
by: Khan, Saiful, et al.
Published: (2025)
Carbon Footprint Evaluation of Code Generation through LLM as a Service
by: Vartziotis, Tina, et al.
Published: (2025)
by: Vartziotis, Tina, et al.
Published: (2025)
FairSense: Long-Term Fairness Analysis of ML-Enabled Systems
by: She, Yining, et al.
Published: (2025)
by: She, Yining, et al.
Published: (2025)
BacPrep: Lessons from Deploying an LLM-Based Bacalaureat Assessment Platform
by: Dumitran, Adrian-Marius, et al.
Published: (2025)
by: Dumitran, Adrian-Marius, et al.
Published: (2025)
Whence Is A Model Fair? Fixing Fairness Bugs via Propensity Score Matching
by: Peng, Kewen, et al.
Published: (2025)
by: Peng, Kewen, et al.
Published: (2025)
Automated Reproducibility Has a Problem Statement Problem
by: Snelleman, Thijs, et al.
Published: (2025)
by: Snelleman, Thijs, et al.
Published: (2025)
To Err is AI : A Case Study Informing LLM Flaw Reporting Practices
by: McGregor, Sean, et al.
Published: (2024)
by: McGregor, Sean, et al.
Published: (2024)
Fairpriori: Improving Biased Subgroup Discovery for Deep Neural Network Fairness
by: Zhou, Kacy, et al.
Published: (2024)
by: Zhou, Kacy, et al.
Published: (2024)
Leveraging Imperfect Sources to Detect Fairwashing in Black-Box Auditing
by: Bourrée, Jade Garcia, et al.
Published: (2023)
by: Bourrée, Jade Garcia, et al.
Published: (2023)
Mining patterns in syntax trees to automate code reviews of student solutions for programming exercises
by: Van Petegem, Charlotte, et al.
Published: (2024)
by: Van Petegem, Charlotte, et al.
Published: (2024)
A Machine Learning-Based Error Mitigation Approach For Reliable Software Development On IBM'S Quantum Computers
by: Muqeet, Asmar, et al.
Published: (2024)
by: Muqeet, Asmar, et al.
Published: (2024)
Using Small Language Models to Reverse-Engineer Machine Learning Pipelines Structures
by: Lacroix, Nicolas, et al.
Published: (2026)
by: Lacroix, Nicolas, et al.
Published: (2026)
Data Requirement Goal Modeling for Machine Learning Systems
by: Yamani, Asma, et al.
Published: (2025)
by: Yamani, Asma, et al.
Published: (2025)
From Pre-labeling to Production: Engineering Lessons from a Machine Learning Pipeline in the Public Sector
by: Ferreira, Ronivaldo, et al.
Published: (2025)
by: Ferreira, Ronivaldo, et al.
Published: (2025)
Predicting Likely-Vulnerable Code Changes: Machine Learning-based Vulnerability Protections for Android Open Source Project
by: Yim, Keun Soo
Published: (2024)
by: Yim, Keun Soo
Published: (2024)
Teaching Requirements Engineering for AI: A Goal-Oriented Approach in Software Engineering Courses
by: Batista, Beatriz, et al.
Published: (2024)
by: Batista, Beatriz, et al.
Published: (2024)
CodeMirage: A Multi-Lingual Benchmark for Detecting AI-Generated and Paraphrased Source Code from Production-Level LLMs
by: Guo, Hanxi, et al.
Published: (2025)
by: Guo, Hanxi, et al.
Published: (2025)
Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns
by: Chen, Keyu, et al.
Published: (2024)
by: Chen, Keyu, et al.
Published: (2024)
Automated Modernization of Machine Learning Engineering Notebooks for Reproducibility
by: Jin, Bihui, et al.
Published: (2026)
by: Jin, Bihui, et al.
Published: (2026)
Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs
by: Iskander, Shadi, et al.
Published: (2024)
by: Iskander, Shadi, et al.
Published: (2024)
A Catalog of Fairness-Aware Practices in Machine Learning Engineering
by: Voria, Gianmario, et al.
Published: (2024)
by: Voria, Gianmario, et al.
Published: (2024)
Suggesting Code Edits in Interactive Machine Learning Notebooks Using Large Language Models
by: Jin, Bihui, et al.
Published: (2025)
by: Jin, Bihui, et al.
Published: (2025)
A Systematic Literature Review on the Use of Machine Learning in Software Engineering
by: Fred, Nyaga, et al.
Published: (2024)
by: Fred, Nyaga, et al.
Published: (2024)
Fairness Testing through Extreme Value Theory
by: Monjezi, Verya, et al.
Published: (2025)
by: Monjezi, Verya, et al.
Published: (2025)
Rethinking Technological Readiness in the Era of AI Uncertainty
by: Browne, S. Tucker, et al.
Published: (2025)
by: Browne, S. Tucker, et al.
Published: (2025)
Measuring Agents in Production
by: Pan, Melissa Z., et al.
Published: (2025)
by: Pan, Melissa Z., et al.
Published: (2025)
Breaking the ICE: Exploring promises and challenges of benchmarks for Inference Carbon & Energy estimation for LLMs
by: Sikand, Samarth, et al.
Published: (2025)
by: Sikand, Samarth, et al.
Published: (2025)
Similar Items
-
Data vs. Model Machine Learning Fairness Testing: An Empirical Study
by: Shome, Arumoy, et al.
Published: (2024) -
FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software
by: Yu, Normen, et al.
Published: (2024) -
Maturity Framework for Enhancing Machine Learning Quality
by: Castelli, Angelantonio, et al.
Published: (2025) -
Risk Management for Mitigating Benchmark Failure Modes: BenchRisk
by: McGregor, Sean, et al.
Published: (2025) -
Machine Learning Models for the Early Detection of Burnout in Software Engineering: a Systematic Literature Review
by: Tulili, Tien Rahayu, et al.
Published: (2026)