arXiv (All)
open access
EDGE: a closed-form directed test for the calibration of probabilistic binary classifiers
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- EDGE: a closed-form directed test for the calibration of probabilistic binary classifiers#656941
- Benchmarking Goodness-of-Fit and Calibration Algorithms for Logistic Regression Classifiers: A Large-Scale Simulation Study under Sparse Data#1155267
- Ternary Decision Trees with Locally-Adaptive Uncertainty Zones#216824
- pico-type: A 1.5M-Parameter Byte-Level Multi-Head Content Classifier#609095
- Partial VOROS: A Cost-aware Performance Metric for Binary Classifiers with Precision and Capacity Constraints.#1001079
Record · ID 659077
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive