The size and complexity of machine learning (ML) models have grown rapidly in recent years. However, the methods for evaluating their performance have not kept pace with this progress. The ML community continues to rely on traditional performance metrics, such as the area under the receiver operating characteristic curve (AUROC) and sensitivity/specificity measures, to assess model performance.
In this article, we argue that relying solely on these metrics provides only a limited understanding of how a model performs and its ability to generalize. We propose that considering scores derived from the test receiver operating characteristic (ROC) curve alone is insufficient and fails to capture the full range of a model’s performance.
We explore alternative approaches for assessing ML model performance and highlight the limitations of relying solely on AUROC and sensitivity/specificity measures. We suggest that incorporating additional metrics, such as precision, recall, and F1 score, can provide a more comprehensive evaluation of model performance.
By broadening our perspective and adopting a multi-metric approach, we can gain deeper insights into the strengths and weaknesses of ML models. This will enable us to make more informed decisions about model deployment and improve the overall reliability and generalizability of ML systems.
The Need for Modern Performance Assessment
As ML models become increasingly complex, relying on traditional performance metrics alone is no longer sufficient. The AUROC and sensitivity/specificity measures fail to capture important aspects of a model’s performance, such as its ability to handle imbalanced datasets and its robustness to different threshold values.
Moreover, focusing solely on the test ROC curve neglects the valuable information provided by the validation ROC curve. By considering both curves, we can better understand how a model generalizes to unseen data and identify potential overfitting or underfitting issues.
Exploring Alternative Metrics
We propose incorporating additional metrics, such as precision, recall, and the F1 score, into the performance assessment of ML models. These metrics provide a more detailed evaluation of model performance, capturing factors such as the trade-off between false positives and false negatives.
By considering a range of metrics, we can gain a more nuanced understanding of a model’s strengths and weaknesses. This enables us to make more informed decisions about model selection and fine-tuning, ultimately improving the overall reliability and usefulness of ML systems.
Conclusion
The ML community must move beyond relying solely on AUROC and sensitivity/specificity measures for performance assessment. By considering scores derived from both the test and validation ROC curves, as well as incorporating additional metrics like precision, recall, and the F1 score, we can obtain a more comprehensive understanding of how a model performs and its ability to generalize. This will pave the way for more effective and reliable ML systems in the future.
Abstract:Whilst the size and complexity of ML models have rapidly and significantly increased over the past decade, the methods for assessing their performance have not kept pace. In particular, among the many potential performance metrics, the ML community stubbornly continues to use (a) the area under the receiver operating characteristic curve (AUROC) for a validation and test cohort (distinct from training data) or (b) the sensitivity and specificity for the test data at an optimal threshold determined from the validation ROC. However, we argue that considering scores derived from the test ROC curve alone gives only a narrow insight into how a model performs and its ability to generalise.