hyperparameter tuning
Beyond Market Intelligence keeps hyperparameter tuning in one place: 3 stories so far. The section currently leads with “Optimize anomaly detection by using all training data in Isolation Forest”, “Astra and Fable 5.1: A practical look at AI spreadsheet tradeoffs”, and “Train smarter by isolating data reuse bias in gradient descent.”. Using all your benign training data in Isolation Forest improved recall from 91% to 94% while cutting false positives from 10% to 7.6%. Two very capable models can still fail differently. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every hyperparameter tuning story on Beyond Market Intelligence, newest first.
Optimize anomaly detection by using all training data in Isolation Forest
Using all your benign training data in Isolation Forest improved recall from 91% to 94% while cutting false positives from 10% to 7.6%. That's a meaningful gain for just 30 extra seconds of training time. The standard advice to keep `max_samples` low applies when anomalies contaminate the training set, your approach is cleaner. Keep `max_samples=1.0`. For deeper insight on detecting subtle shifts in feature relationships, see our related article on spotting hidden data drift.
Astra and Fable 5.1: A practical look at AI spreadsheet tradeoffs
Two very capable models can still fail differently. In a side-by-side ML workflow, Astra and Fable 5.1 both improved by 0.02-0.04 F1 after human feedback, proving neither has mastered the process. Astra wins on agentic debugging and reproducibility, while Fable writes cleaner code and more insightful analysis. The real lesson? Pick your tool based on whether you need forensic rigor or readable, adaptable output. For deeper context on model tradeoffs, our related piece, "Explore the Forrester Function," explores similar evaluation themes.
Train smarter by isolating data reuse bias in gradient descent.
Training a neural network until the training error vanishes while the test error stagnates is a familiar frustration. *Decoupled Descent* frames this as data reuse bias, isolating it with full-batch gradient descent on Gaussian mixture models. By applying approximate message passing corrections, the method guarantees that training and test errors asymptotically align at every iterate. It's a theory paper, so large-scale models remain a stretch, but the certificate it offers is a thoughtful step toward principled stopping.