Wikis
Info-nuggets to help anyone understand various concepts of MLOps, their significance, and how they are managed throughout the ML lifecycle.
LOCO
The LOCO method seeks to understand the significance of a particular feature for a model's prediction performance.
The Leave-One-Covariate-Out (LOCO) method seeks to understand the significance of a particular feature for a model's prediction performance. By calculating the mean change in accuracy for each variable throughout the entire data set, LOCO generates global variable importance measures and can provide confidence intervals.
Is Explainability critical for your AI solutions?
Schedule a demo with our team to understand how AryaXAI can make your mission-critical 'AI' acceptable and aligned with all your stakeholders.
AryaXAI provides the most accurate explainability and alignment stack to deliver accurate, true-to-model explainability, monitoring, risk management, and alignment techniques essential for highly mission-critical or regulated AI solutions.
Products
Resources
Follow Us
Follow Us
