Model validation: Performance and metrics

Episode 9. Continuing our series run about model validation. In this episode, the hosts focus on aspects of performance, why we need to do statistics correctly, and not use metrics without understanding how they work, to ensure that models are evaluated in a meaningful way.


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.

Related Information

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

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