Recognizing Hidden Assumptions in Evaluation
When evaluating machine learning models, you often rely on a set of assumptions about the data and the evaluation process. Some of these assumptions are explicit, such as the expectation that the training and test data are drawn from the same distribution, but others are more subtle and can easily go unnoticed. Two of the most commonly overlooked assumptions in evaluation pipelines are stationarity and representativeness.
Hidden assumptions like these can lead to misleading conclusions about model performance. For example, you might assume that the data distribution remains constant over time (stationarity), or that your test set accurately reflects the data the model will encounter in the real world (representativeness). When these assumptions do not hold, your evaluation metrics may no longer be reliable indicators of future performance.
In the context of evaluation, stationarity means that the statistical properties of the data—such as mean, variance, and distribution—do not change over time or across different environments.
Representativeness refers to the assumption that the evaluation or test set accurately mirrors the real-world data distribution the model will face after deployment.
To help you identify these hidden assumptions in your own workflows, consider the following checklist:
- Check whether the data sources for training and testing are truly independent and identically distributed;
- Examine if there are any trends or seasonality in the data that could break the stationarity assumption;
- Confirm that the test set covers the same range of input conditions as the expected deployment environment;
- Investigate whether the process of splitting data into train and test sets might have introduced sampling bias;
- Review if any preprocessing steps applied to the data could have altered its distribution in unintended ways;
- Monitor for changes in data collection methods over time that could affect stationarity or representativeness;
- Regularly validate that evaluation metrics remain stable as new data is collected and processed.
By systematically applying this checklist, you can better recognize when hidden assumptions might be affecting your evaluation results. This awareness is crucial for building robust models that perform reliably in real-world scenarios.
Obrigado pelo seu feedback!
Pergunte à IA
Pergunte à IA
Pergunte o que quiser ou experimente uma das perguntas sugeridas para iniciar nosso bate-papo
Can you explain more about what stationarity means in this context?
How can I detect if my data is not representative?
What are some common ways to address these hidden assumptions in practice?
Incrível!
Completion taxa melhorada para 10Recognizing Hidden Assumptions in Evaluation
Deslize para mostrar o menu
When evaluating machine learning models, you often rely on a set of assumptions about the data and the evaluation process. Some of these assumptions are explicit, such as the expectation that the training and test data are drawn from the same distribution, but others are more subtle and can easily go unnoticed. Two of the most commonly overlooked assumptions in evaluation pipelines are stationarity and representativeness.
Hidden assumptions like these can lead to misleading conclusions about model performance. For example, you might assume that the data distribution remains constant over time (stationarity), or that your test set accurately reflects the data the model will encounter in the real world (representativeness). When these assumptions do not hold, your evaluation metrics may no longer be reliable indicators of future performance.
In the context of evaluation, stationarity means that the statistical properties of the data—such as mean, variance, and distribution—do not change over time or across different environments.
Representativeness refers to the assumption that the evaluation or test set accurately mirrors the real-world data distribution the model will face after deployment.
To help you identify these hidden assumptions in your own workflows, consider the following checklist:
- Check whether the data sources for training and testing are truly independent and identically distributed;
- Examine if there are any trends or seasonality in the data that could break the stationarity assumption;
- Confirm that the test set covers the same range of input conditions as the expected deployment environment;
- Investigate whether the process of splitting data into train and test sets might have introduced sampling bias;
- Review if any preprocessing steps applied to the data could have altered its distribution in unintended ways;
- Monitor for changes in data collection methods over time that could affect stationarity or representativeness;
- Regularly validate that evaluation metrics remain stable as new data is collected and processed.
By systematically applying this checklist, you can better recognize when hidden assumptions might be affecting your evaluation results. This awareness is crucial for building robust models that perform reliably in real-world scenarios.
Obrigado pelo seu feedback!
