Fairness Hub Technical Briefs: Definition and Detection of Distribution Shift
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916257516748800 |
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| author | Acevedo, Nicolas Cortez, Carmen Brooks, Chris Kizilcec, Rene Yu, Renzhe |
| author_facet | Acevedo, Nicolas Cortez, Carmen Brooks, Chris Kizilcec, Rene Yu, Renzhe |
| contents | Distribution shift is a common situation in machine learning tasks, where the data used for training a model is different from the data the model is applied to in the real world. This issue arises across multiple technical settings: from standard prediction tasks, to time-series forecasting, and to more recent applications of large language models (LLMs). This mismatch can lead to performance reductions, and can be related to a multiplicity of factors: sampling issues and non-representative data, changes in the environment or policies, or the emergence of previously unseen scenarios. This brief focuses on the definition and detection of distribution shifts in educational settings. We focus on standard prediction problems, where the task is to learn a model that takes in a series of input (predictors) $X=(x_1,x_2,...,x_m)$ and produces an output $Y=f(X)$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_14186 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fairness Hub Technical Briefs: Definition and Detection of Distribution Shift Acevedo, Nicolas Cortez, Carmen Brooks, Chris Kizilcec, Rene Yu, Renzhe Machine Learning Computers and Society Distribution shift is a common situation in machine learning tasks, where the data used for training a model is different from the data the model is applied to in the real world. This issue arises across multiple technical settings: from standard prediction tasks, to time-series forecasting, and to more recent applications of large language models (LLMs). This mismatch can lead to performance reductions, and can be related to a multiplicity of factors: sampling issues and non-representative data, changes in the environment or policies, or the emergence of previously unseen scenarios. This brief focuses on the definition and detection of distribution shifts in educational settings. We focus on standard prediction problems, where the task is to learn a model that takes in a series of input (predictors) $X=(x_1,x_2,...,x_m)$ and produces an output $Y=f(X)$. |
| title | Fairness Hub Technical Briefs: Definition and Detection of Distribution Shift |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2405.14186 |