Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.17926 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910350089125888 |
|---|---|
| author | Zhen, Cheng Aryal, Nischal Termehchy, Arash Aghasi, Alireza Chabada, Amandeep Singh |
| author_facet | Zhen, Cheng Aryal, Nischal Termehchy, Arash Aghasi, Alireza Chabada, Amandeep Singh |
| contents | Real-world data is often incomplete and contains missing values. To train accurate models over real-world datasets, users need to spend a substantial amount of time and resources imputing and finding proper values for missing data items. In this paper, we demonstrate that it is possible to learn accurate models directly from data with missing values for certain training data and target models. We propose a unified approach for checking the necessity of data imputation to learn accurate models across various widely-used machine learning paradigms. We build efficient algorithms with theoretical guarantees to check this necessity and return accurate models in cases where imputation is unnecessary. Our extensive experiments indicate that our proposed algorithms significantly reduce the amount of time and effort needed for data imputation without imposing considerable computational overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17926 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Certain and Approximately Certain Models for Statistical Learning Zhen, Cheng Aryal, Nischal Termehchy, Arash Aghasi, Alireza Chabada, Amandeep Singh Machine Learning Databases Real-world data is often incomplete and contains missing values. To train accurate models over real-world datasets, users need to spend a substantial amount of time and resources imputing and finding proper values for missing data items. In this paper, we demonstrate that it is possible to learn accurate models directly from data with missing values for certain training data and target models. We propose a unified approach for checking the necessity of data imputation to learn accurate models across various widely-used machine learning paradigms. We build efficient algorithms with theoretical guarantees to check this necessity and return accurate models in cases where imputation is unnecessary. Our extensive experiments indicate that our proposed algorithms significantly reduce the amount of time and effort needed for data imputation without imposing considerable computational overhead. |
| title | Certain and Approximately Certain Models for Statistical Learning |
| topic | Machine Learning Databases |
| url | https://arxiv.org/abs/2402.17926 |