- Published
- 2022-04-04
- Venue
- Statistical Analysis and Data Mining The ASA Data Science Journal, vol. 15, no. 4, pp. 531-538
- Cited by
- 786
- Open access
- Yes, hybrid
- DOI
- 10.1002/sam.11583
Abstract
It is common to split a dataset into training and testing sets before fitting a statistical or machine learning model. However, there is no clear guidance on how much data should be used for training and testing. In this article, we show that the optimal training/testing splitting ratio is , where is the number of parameters in a linear regression model that explains the data well.
Cite this paper
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- APA 7
Joseph, V. R. (2022). Optimal ratio for data splitting. Statistical Analysis and Data Mining The ASA Data Science Journal, 15(4), 531–538. https://doi.org/10.1002/sam.11583
- MLA 9
Joseph, V. Roshan. "Optimal ratio for data splitting." Statistical Analysis and Data Mining The ASA Data Science Journal, vol. 15, no. 4, 2022, pp. 531–538, https://doi.org/10.1002/sam.11583.
- Chicago
Joseph, V. Roshan. "Optimal ratio for data splitting." Statistical Analysis and Data Mining The ASA Data Science Journal 15, no. 4 (2022): 531–538. https://doi.org/10.1002/sam.11583.
- GOST
Joseph V. R. Optimal ratio for data splitting // Statistical Analysis and Data Mining The ASA Data Science Journal. — 2022. — Vol. 15, № 4. — P. 531–538. — URL: https://doi.org/10.1002/sam.11583
- BibTeX
@article{joseph2022optimal, title = {{Optimal ratio for data splitting}}, author = {V. Roshan Joseph}, journal = {Statistical Analysis and Data Mining The ASA Data Science Journal}, year = {2022}, volume = {15}, number = {4}, pages = {531--538}, doi = {10.1002/sam.11583}, url = {https://doi.org/10.1002/sam.11583} }- RIS
TY - JOUR AU - Joseph, V. Roshan TI - Optimal ratio for data splitting JO - Statistical Analysis and Data Mining The ASA Data Science Journal VL - 15 IS - 4 SP - 531 EP - 538 PY - 2022 DA - 2022/04/04 DO - 10.1002/sam.11583 UR - https://doi.org/10.1002/sam.11583 ER -