model validation

Random Subsampling (RSS) - Monte Carlo Cross-Validation (MCCV) is a versatile and powerful technique in the field of machine learning and model evaluation. This method stands out as a robust approach for estimating a model's performance and generalization abilities, especially when dealing with limited or imbalanced data. Combining the principles of random subsampling and Monte Carlo simulation, RSS-MCCV offers an efficient and unbiased way to assess model performance in situations where traditional cross-validation may be impractical or computationally expensive.

The key steps involved in Random Subsampling - Monte Carlo Cross-Validation are as follows:

1. Data Splitting: The initial dataset is randomly divided into two subsets: a training set and a test set. The training set is used to train the machine learning model, while the test set is reserved for evaluating its performance.
2. Model Training and Evaluation: The machine learning model is trained on the training set, and its performance is assessed on the test set using relevant evaluation metrics (e.g., accuracy, precision, recall, F1-score).
3. Iteration: The above steps are repeated for a specified number of iterations (often denoted as "n"), each time with a new random split of the data. This randomness introduces diversity in the subsets used for training and testing.
4. Performance Metrics Aggregation: After all iterations are complete, the performance metrics (e.g., accuracy scores) obtained from each iteration are typically aggregated. This aggregation can include calculating means, standard deviations, or other statistical measures to summarize the model's overall performance.

The distinctive characteristics and advantages of RSS-MCCV include:

• Efficiency: RSS-MCCV is computationally efficient, especially when compared to exhaustive cross-validation techniques like Leave-One-Out Cross-Validation (LOOCV). It can provide reliable performance estimates without the need to train and evaluate models on all possible combinations of data partitions.
• Flexibility: This method adapts well to various data scenarios, including small datasets, imbalanced class distributions, and when the dataset's inherent structure makes traditional k-fold cross-validation challenging.
• Monte Carlo Simulation: By incorporating randomization and repeated sampling, RSS-MCCV leverages Monte Carlo principles, allowing for a more robust estimation of model performance, particularly when dealing with limited data.
• Bias Reduction: RSS-MCCV helps reduce potential bias that can arise from single, fixed splits of the data, ensuring a more representative assessment of a model's ability to generalize.

Kind regards Jörg-Owe Schneppat & GPT 5

Leave-One-Out Cross-Validation (LOOCV) is a method used in machine learning to evaluate the performance of predictive models. It is a special case of k-fold cross-validation, where the number of folds (k) equals the number of data points in the dataset. This technique is particularly useful for small datasets or when an exhaustive assessment of the model's performance is desired.

Understanding LOOCV

In LOOCV, the dataset is partitioned such that each instance, or data point, gets its turn to be the validation set, while the remaining data points form the training set. This process is repeated for each data point, meaning the model is trained and validated as many times as there are data points.

Key Steps in LOOCV

1. Partitioning the Data: For a dataset with N instances, the model undergoes N separate training phases. In each phase, N-1 instances are used for training, and a single, different instance is used for validation.
2. Training and Validation: In each iteration, the model is trained on the N-1 instances and validated on the single left-out instance. This helps in assessing how the model performs on unseen data.
3. Performance Metrics: After each training and validation step, performance metrics (like accuracy, precision, recall, F1-score, or mean squared error) are recorded.
4. Aggregating Results: The performance metrics across all iterations are averaged to provide an overall performance measure of the model.

Challenges and Limitations

• Computational Cost: LOOCV can be computationally intensive, especially for large datasets, as the model needs to be trained N times.
• High Variance in Model Evaluation: The results can have high variance, especially if the dataset contains outliers or if the model is very sensitive to the specific training data used.

Applications of LOOCV

LOOCV is often used in situations where the dataset is small and losing even a small portion of the data for validation (as in k-fold cross-validation) would be detrimental to the model training. It is also applied in scenarios requiring detailed and exhaustive model evaluation.

Conclusion: A Comprehensive Tool for Model Assessment

LOOCV serves as a comprehensive tool for assessing the performance of predictive models, especially in scenarios where every data point's contribution to the model's performance needs to be evaluated. While it is computationally demanding, the insights gained from LOOCV can be invaluable, particularly for small datasets or in cases where an in-depth understanding of the model's behavior is crucial.

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Kind regards J.O. Schneppat & GPT-5

Episode 8. This is the first in a series of episodes dedicated to model validation. Today, we focus on model robustness and resilience. From complex financial systems to why your gym might be overcrowded at New Year's, you've been directly affected by these aspects of model validation.

AI hype and consumer trust (0:03) 

• FTC article highlights consumer concerns about AI's impact on lives and businesses (Oct 3, FTC)
• Increased public awareness of AI and the masses of data needed to train it led to increased awareness of potential implications for misuse.
• Need for transparency and trust in AI's development and deployment.

Model validation and its importance in AI development (3:42)

• Importance of model validation in AI development, ensuring models are doing what they're supposed to do.
• FTC's heightened awareness of responsibility and the need for fair and unbiased AI practices.
• Model validation (targeted, specific) vs model evaluation (general, open-ended).

Model validation and resilience in machine learning (8:26)

• Collaboration between engineers and businesses to validate models for resilience and robustness.
• Resilience: how well a model handles adverse data scenarios.
• Robustness: model's ability to generalize to unforeseen data.
• Aerospace Engineering: models must be resilient and robust to perform well in real-world environments.

Statistical evaluation and modeling in machine learning (14:09)

• Statistical evaluation involves modeling distribution without knowing everything, using methods like Monte Carlo sampling.
• Monte Carlo simulations originated in physics for assessing risk and uncertainty in decision-making.

Monte Carlo methods for analyzing model robustness and resilience (17:24)

• Monte Carlo simulations allow exploration of potential input spaces and estimation of underlying distribution.
• Useful when analytical solutions are unavailable.
• Sensitivity analysis and uncertainty analysis as major flavors of analyses.

Monte Carlo techniques and model validation (21:31)

• Versatility of Monte Carlo simulations in various fields.
• Using Monte Carlo experiments to explore semantic space vectors of language models like GPT.
• Importance of validating machine learning models through negative scenario analysis.

Stress testing and resiliency in finance and engineering (25:48)

• Importance of stress testing in finance, combining traditional methods with Monte Carlo techniques.
• Synthetic data's potential in modeling critical systems.
• Identifying potential gaps and vulnerabilities in critical systems.

Using operations research and model validation in AI development (30:13)

• Operations research can help find an equilibrium in overcrowding in gyms.
• Robust methods for solving complex problems in logistics and healthcare.
• Model validation's importance in addressing issues of bias and fairness in AI systems.

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This week I welcome Dr. Andrew Clark, Co-founder & CTO of Monitaur, a trusted domain expert on the topic of machine learning, auditing and assurance; and Sid Mangalik, Research Scientist at Monitaur and PhD student at Stony Brook University. I discovered Andrew and Sid's new podcast show, The AI Fundamentalists Podcast. I very much enjoyed their lively episode on Synthetic Data & AI, and am delighted to introduce them to my audience of privacy engineers.

In our conversation, we explore why data scientists must stress test their model validations, especially for consequential systems that affect human safety and reliability. In fact, we have much to learn from the aerospace engineering field who has been using ML/AI since the 1960s. We discuss the best and worst use cases for using synthetic data'; problems with LLM-generated synthetic data; what can go wrong when your AI models lack diversity; how to build fair, performant systems; & synthetic data techniques for use with AI.

Topics Covered:

• What inspired Andrew to found Monitaur and focus on AI governance
• Sid’s career path and his current PhD focus on NLP
• What motivated Andrew & Sid to launch their podcast, The AI Fundamentalists
• Defining 'synthetic data' & why academia takes a more rigorous approach to synthetic data than industry
• Whether the output of LLMs are synthetic data & the problem with training LLM base models with this data
• The best and worst 'synthetic data' use cases for ML/AI
• Why the 'quality' of input data is so important when training AI models 
• Thoughts on OpenAI's announcement that it will use LLM-generated synthetic data; and critique of OpenAI's approach, the AI hype machine, and the problems with 'growth hacking' corner-cutting
• The importance of diversity when training AI models; using 'multi-objective modeling' for building fair & performant systems
• Andrew unpacks the "fairness through unawareness fallacy"
• How 'randomized data' differs from 'synthetic data'
• 4 techniques for using synthetic data with ML/AI: 1) the Monte Carlo method; 2) Latin hypercube sampling; 3) gaussian copulas; & 4) random walking
• What excites Andrew & Sid about synthetic data and how it will be used with AI in the future

Resources Mentioned:

• Check out Podchaser 
• Listen to The AI Fundamentalists Podcast
• Check out Monitaur

Guest Info:

• Follow Andrew on LinkedIn
• Follow Sid on LinkedIn

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Often we focus on models so much that we forget they aren't perfect. There is a strong need for a validation team to check the work of model development. However this leads to disagreements between teams. There are many factors that play into this which include technical limitations, limitations of employee education and experience, usage limitations, and ego. Quants make their money based on their intelligence. It is hard to admit you either made a mistake or do have the knowledge required to model a specific problem. Add on the requirement to do additional work and potential negative impacts to your promotions and bonuses and we end up in a hostile environment. These limitations are risks in themselves and should be viewed as part of model risk management's analysis.

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