arXiv (All)
open access
Impact Range Assessment (IRA): An Interpretable Sensitivity Measure for Machine Learning Regression Modelling
While machine learning regression models capture the relationship between predictors and the response variable, they often lack intuitive methods to help modelers understand how sensitive model predictions are to changes in individual predictors. To address this limitation, we introduce a model-agno
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
- Beyond Global Importance: A Heterogeneity Index for Sensitivity Analysis#1424126
- Validating Explainer Methods: A Functionally Grounded Approach for Numerical Forecasting#451792
- Fast Computation of Nested Cross-Validation for Penalized Regression#985274
- ACTIVA: Amortized Causal Effect Estimation via Variational Autoencoders#1423693
- Benchmarking EEG Foundation Models at Scale: Lessons from 20,000 Evaluations#1117074
Record · ID 1536076
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