MAE
MAE on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on mae in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around mae, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Open-source Python library + no-code web dashboard for evaluating oncology AI models at clinical decision thresholds. [P]
Clinical decision-making in oncology hinges on reliable AI model performance *at specific thresholds*, a critical evaluation often overlooked by standard metrics like AUC. Oncothresh, a dependency-light Python library, addresses this gap by providing precise assessments of sensitivity, specificity, and related metrics at defined cutoffs, alongside confidence intervals and decision-curve analysis. A companion no-code web dashboard simplifies this analysis—simply upload data and generate comprehensive reports. Explore Oncothresh and oncothresh-web (github.com/omkaradhali/oncothresh) and contribute feedback to refine this valuable tool.
UrgenT Help Detecting Performance Regressions Using Machine Learning and Hardware Counters [P]
Detecting performance regressions demands a robust evaluation strategy. This post explores a common challenge: building a machine learning model for anomaly detection with limited "healthy" data—specifically, around 10 samples per counter group. The author's approach, utilizing leave-one-out for threshold setting and treating regression samples as a test set, raises key questions regarding optimal validation splits and evaluation metrics. Prioritizing false-positive and detection rates over traditional MSE/MAE is crucial in this one-class anomaly detection scenario.