Model validation: Performance and metrics

Summary

AI regulations, red team testing, and physics-based modeling. 0:03

• Andrew and Sid discuss the Biden administration's executive order on AI and its implications for model validation and performance.
• The order emphasizes the use of the NIST AI risk management framework (RMF) and includes provisions for red team testing to evaluate models against adversarial attacks.
• Andrew highlights the requirement for companies to notify the federal government when training AI models that pose a risk to national security, national economic security, or national public health and safety.
• Susan notes that the executive order is significant for the US, particularly in comparison to global efforts like the EU's EUAI act, and that its impact will be wait-and-see.
• Andrew and Susan discuss the limitations of current AI models and the need for more scientifically-based approaches.
• They highlight the importance of model validation and performance, particularly in the context of digital twin simulations and jet engines.

Evaluating machine learning models using accuracy, recall, and precision. 6:52

• Classification models evaluate accuracy by comparing predicted vs actual values in a confusion matrix.
• Classification metrics depend on use case and can include accuracy, precision, recall, F1 score, and AUC-ROC.
• The four types of results in classification: true positive, false positive, true negative, and false negative.
• The three standard metrics composed of these elements: accuracy, recall, and precision.

Accuracy metrics for classification models. 12:36

• Sid explains that precision and recall are interrelated aspects of accuracy in machine learning.
• Andrew discusses the importance of using F1 score and F beta score in classification models, particularly when dealing with imbalanced data.
• F beta score allows for weighting recall and precision based on use case, providing a more nuanced evaluation of model performance.

Performance metrics for regression tasks. 17:08

• Sid explains the importance of handling imbalanced outcomes in machine learning, particularly in regression tasks.
• Andrew discusses the different metrics used to evaluate regression models, including mean squared error.

Performance metrics for machine learning models. 19:56

• Mean squared error (MSE) as a metric for evaluating the accuracy of machine learning models, using the example of predicting house prices.
• Mean absolute error (MAE) as an alternative metric, which penalizes large errors less heavily and is more straightforward to compute.
• MSE is a valuable metric for machine learning models due to its differentiability, which allows for efficient optimization using gradient descent.
• Sid highlights the interpretability issue with regression models, and how R-squared and adjusted R-squared provide a better understanding of model performance.

Graph theory and operations research applications. 25:48

• The use of graph theory in machine learning, including the shortest path problem and clustering, and Sid Mangalik adds that Euclidean distance is a popular benchmark for measuring distances between data points.
• Distributional testing, including nonparametric and parametric tests, and how they can be used to compare different distributions.
• The difference between distributional testing and classification testing, and why certain tests are more appropriate for different types of data.
• Andrew highlights the efficiency and accuracy of graph theory and operations research in solving complex logistical problems, such as finding the fastest path between points.
• He contrasts this with the Wild West approach of machine learning, where there are no rules and bad performance is often accepted due to hubris and a lack of understanding of existing theory.

Machine learning metrics and evaluation methods. 33:06

• AUC and Roc fall out of industry problem of not wanting to determine data for F1 score or precision recall balance, leading to popularity of AUC as a single score metric.
• Cross-validation is a technique used to evaluate machine learning models by training and testing them on different subsets of the data, providing a more accurate assessment of the model's performance.
• Entropy is a measure of the amount of information gained from a single cut in a decision tree, and is used to evaluate the quality of the tree's splits and the features used to make those splits.

Model validation using statistics and information theory. 37:08

• Entropy, its roots in classical mechanics and thermodynamics, and its application in information theory, particularly Shannon entropy calculation.
• Clark explains how Shannon entropy calculation can be used to identify the most important metrics or Monte Carlo runs by measuring their information value, with higher values indicating unexpected events.
• The importance of understanding information theory in model validation, as it can provide valuable insights and potentially be the best solution for a problem.
• Sid emphasizes the need to do statistics correctly and not use metrics without understanding how they work, to ensure that models are evaluated in a meaningful way.
• Sid and Andrew discuss the importance of understanding the use case and validation metrics for machine learning models.
• The hosts invite listeners to provide feedback on past episodes and suggest topics for future episodes, including a focus on bias in model validation.

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Related Information

For more about model validation, see our previous episode about model robustness and resilience.

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