Tag: when
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Synthetic Augmentation in Imbalanced Learning: When It Helps, When It Hurts, and How Much to Add
Synthetic Augmentation in Imbalanced Learning: When It Helps, When It Hurts, and How Much to Add arXiv:2601.16120v1 Announce Type: new Abstract: Imbalanced classification, where one class is observed far less frequently than the other, often causes standard training procedures to prioritize the majority class and perform poorly on rare but important cases. A classic and…
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When Does Adding Fancy RAG Features Work?
When Does Adding Fancy RAG Features Work? Looking at the performance of different pipelines The post When Does Adding Fancy RAG Features Work? appeared first on Towards Data Science. Ida Silfverskiöld Go to original source
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What to Do When Your Credit Risk Model Works Today, but Breaks Six Months Later
What to Do When Your Credit Risk Model Works Today, but Breaks Six Months Later Here’s why it happens — and how to fix it The post What to Do When Your Credit Risk Model Works Today, but Breaks Six Months Later appeared first on Towards Data Science. Javier Marin Go to original source
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When A Difference Actually Makes A Difference
When A Difference Actually Makes A Difference Bite-Sized Analytics for Business Decision-Makers (1) The post When A Difference Actually Makes A Difference appeared first on Towards Data Science. Mena Wang Go to original source
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The Relative Instability of Model Comparison with Cross-validation
The Relative Instability of Model Comparison with Cross-validation arXiv:2508.04409v1 Announce Type: new Abstract: Existing work has shown that cross-validation (CV) can be used to provide an asymptotic confidence interval for the test error of a stable machine learning algorithm, and existing stability results for many popular algorithms can be applied to derive positive instances where…
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When Models Stop Listening: How Feature Collapse Quietly Erodes Machine Learning Systems
When Models Stop Listening: How Feature Collapse Quietly Erodes Machine Learning Systems Models don’t just fail with noise; they fail in silence, by narrowing their attention to the point of fragility. The post When Models Stop Listening: How Feature Collapse Quietly Erodes Machine Learning Systems appeared first on Towards Data Science. Mahe Jabeen Abdul Go…
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When 50/50 Isn’t Optimal: Debunking Even Rebalancing
When 50/50 Isn’t Optimal: Debunking Even Rebalancing A new theory of class imbalance demonstrates that the optimal training imbalance in a binary problem is not 50% The post When 50/50 Isn’t Optimal: Debunking Even Rebalancing appeared first on Towards Data Science. Marco Baity-Jesi Go to original source
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The Automation Trap: Why Low-Code AI Models Fail When You Scale
The Automation Trap: Why Low-Code AI Models Fail When You Scale In the beginning, building Machine Learning models was a skill only data scientists with knowledge of Python could master. However, low-code AI platforms have made things much easier now. Anyone can now directly make a model, link it to data, and publish it as…
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The Shadow Side of AutoML: When No-Code Tools Hurt More Than Help
The Shadow Side of AutoML: When No-Code Tools Hurt More Than Help Automl has become the gateway drug to machine learning for many organizations. It promises exactly what teams under pressure want to hear: you bring the data, and we’ll handle the modeling. There are no pipelines to manage, no hyperparameters to tune, and no…
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Regression Discontinuity Design: How It Works and When to Use It
Regression Discontinuity Design: How It Works and When to Use It Regression Discontinuity Design: How It Works and When to Use It You’re an avid data scientist and experimenter. You know that randomisation is the summit of Mount Evidence Credibility, and you also know that when you can’t randomise, you resort to observational data and…
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When OpenAI Isn’t Always the Answer: Enterprise Risks Behind Wrapper-Based AI Agents
When OpenAI Isn’t Always the Answer: Enterprise Risks Behind Wrapper-Based AI Agents “Wait… are you sending journal entries to OpenAI?” That was the first thing my friend asked when I showed her Feel-Write, an AI-powered journaling app I built during a hackathon in San Francisco. I shrugged. “It was an AI-themed hackathon, I had to…
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Mastering the Poisson Distribution: Intuition and Foundations
Mastering the Poisson Distribution: Intuition and Foundations You’ve probably used the normal distribution one or two times too many. We all have — It’s a true workhorse. But sometimes, we run into problems. For instance, when predicting or forecasting values, simulating data given a particular data-generating process, or when we try to visualise model output…
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Where to Start when Data is Limited: A Guide
Where to Start when Data is Limited: A Guide Hey, I’ve put together an article on my thoughts and some research around how to get the most out of small datasets when performance requirements mean conventional analysis isn’t enough. It’s aimed at helping people get started with new projects who have already started with the…
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When Averages Lie: Moving Beyond Single-Point Predictions
When Averages Lie: Moving Beyond Single-Point Predictions The Case for Predicting Full Probability Distributions in Decision-Making Some people like hot coffee, some people like iced coffee, but no one likes lukewarm coffee. Yet, a simple model trained on coffee temperatures might predict that the next coffee served should be… lukewarm. This illustrates a fundamental problem…
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ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation
ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation arXiv:2411.19090v1 Announce Type: new Abstract: Algorithmic bias continues to be a key concern of learning analytics. We study the statistical properties of the Absolute Between-ROC Area (ABROCA) metric. This fairness measure quantifies group-level differences in classifier performance through the absolute difference in ROC curves. ABROCA is…