model performance

Nested Cross-Validation (nCV) is a sophisticated and essential technique in the field of machine learning and model evaluation. It is specifically designed to provide a robust and unbiased estimate of a model's performance and generalization capabilities, addressing the challenges of hyperparameter tuning and model selection. In essence, nCV takes cross-validation to a higher level of granularity, allowing practitioners to make more informed decisions about model architectures and hyperparameter settings.

The primary motivation behind nested cross-validation lies in the need to strike a balance between model complexity and generalization. In machine learning, models often have various hyperparameters that need to be fine-tuned to achieve optimal performance. These hyperparameters can significantly impact a model's ability to generalize to new, unseen data. However, choosing the right combination of hyperparameters can be a challenging task, as it can lead to overfitting or underfitting if not done correctly.

Nested Cross-Validation addresses this challenge through a nested structure that comprises two layers of cross-validation: an outer loop and an inner loop. Here's how the process works:

1. Outer Loop: Model Evaluation

• The dataset is divided into multiple folds (usually k-folds), just like in traditional k-fold cross-validation.
• The outer loop is responsible for model evaluation. It divides the dataset into training and test sets for each fold.
• In each iteration of the outer loop, one fold is held out as the test set, and the remaining folds are used for training.
• A model is trained on the training folds using a specific set of hyperparameters (often chosen beforehand or through a hyperparameter search).
• The model's performance is then evaluated on the held-out fold, and a performance metric (such as accuracy, mean squared error, or F1-score) is recorded.

2. Inner Loop: Hyperparameter Tuning

• The inner loop operates within each iteration of the outer loop and is responsible for hyperparameter tuning.
• The training folds from the outer loop are further divided into training and validation sets.
• Multiple combinations of hyperparameters are tested on the training and validation sets to find the best-performing set of hyperparameters for the given model.
• The hyperparameters that result in the best performance on the validation set are selected.

3. Aggregation and Analysis

• After completing the outer loop, performance metrics collected from each fold's test set are aggregated, typically by calculating the mean and standard deviation.
• This aggregated performance metric provides an unbiased estimate of the model's generalization capability.
• Additionally, the best hyperparameters chosen during the inner loop can inform the final model selection, as they represent the hyperparameters that performed best across multiple training and validation sets.

Kind regards J.O. Schneppat & GPT 5

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
  • The hosts discuss the Biden administration's executive order on AI and its implications for model validation and performance.
• Evaluating machine learning models using accuracy, recall, and precision. 6:52
  • The four types of results in classification: true positive, false positive, true negative, and false negative.
  • The three standard metrics are composed of these elements: accuracy, recall, and precision.
• Accuracy metrics for classification models. 12:36
  • Precision and recall are interrelated aspects of accuracy in machine learning.
  • Using F1 score and F beta score in classification models, particularly when dealing with imbalanced data.
• Performance metrics for regression tasks. 17:08
  • Handling imbalanced outcomes in machine learning, particularly in regression tasks.
  • 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.
• Graph theory and operations research applications. 25:48
  • Graph theory in machine learning, including the shortest path problem and clustering. Euclidean distance is a popular benchmark for measuring distances between data points. 
• Machine learning metrics and evaluation methods. 33:06
• 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. 
  • The importance the use case and validation metrics for machine learning models.

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In this episode, Stefan Zeuzem, MD, discusses new data on viral hepatitis presented at EASL 2023, including:

• Hepatitis B virus
  • Durability of response with bepirovirsen
  • HBsAg loss with siRNA VIR-2218 combined with either VIR-3434 (novel monoclonal antibody) or pegIFN-alfa
• Hepatitis delta virus
  • 96-week follow-up of immediate vs delayed bulevirtide
  • Off-treatment response for lonafarnib + ritonavir ± pegIFN-alfa 
  • Safety and efficacy outcomes with siRNA JNJ-3989 + nucleos(t)ide analogue
• Hepatitis C virus
  • Collaborative service at opiate substitution treatment clinic to improve linkage to care in Ireland
  • Nurse-led test-and-treat program to increase screening and diagnosis at female prisons in the United Kingdom
  • FIND-C study using machine learning to improve screening-to-diagnosis ratio using clinical factors and social determinants of health

Presenter:

Stefan Zeuzem, MD
Professor of Medicine 
Chief, Department of Medicine 
JW Goethe University Hospital 
Frankfurt, Germany

Link to full program: 

https://bit.ly/3JQQj3J

Dive into this episode of The AI Frontier podcast, where we explore Ensemble Learning techniques like Boosting, Bagging, and Random Forests in Machine Learning. Learn about their applications, advantages, and limitations, and discover real-world success stories. Enhance your understanding of these powerful methods and stay ahead in the world of data science.

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