From 93eb17925b3b35ae9ce31312e576835c6103ab80 Mon Sep 17 00:00:00 2001 From: Kishan Savant Date: Wed, 21 Feb 2024 23:25:15 +0530 Subject: [PATCH] Fixed with correct link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3efd9294..54da26e0 100644 --- a/README.md +++ b/README.md @@ -45,7 +45,7 @@ NannyML is an open-source python library that allows you to **estimate post-deployment model performance** (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, interactive visualizations, is completely model-agnostic and currently supports all tabular use cases, classification and **regression**. The core contributors of NannyML have researched and developed multiple novel algorithms for estimating model performance: [confidence-based performance estimation (CBPE)](https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html#confidence-based-performance-estimation-cbpe) and [direct loss estimation (DLE)](https://nannyml.readthedocs.io/en/stable/how_it_works/performance_estimation.html#direct-loss-estimation-dle). -The nansters also invented a new approach to detect [multivariate data drift](https://nannyml.readthedocs.io/en/stable/how_it_works/multivariate_drift.html) using PCA-based data reconstruction. +The nansters also invented a new approach to detect [multivariate data drift](https://nannyml.readthedocs.io/en/stable/how_it_works/multivariate_drift.html#data-reconstruction-with-pca) using PCA-based data reconstruction. If you like what we are working on, be sure to become a Nanster yourself, join our [community slack](https://join.slack.com/t/nannymlbeta/shared_invite/zt-16fvpeddz-HAvTsjNEyC9CE6JXbiM7BQ) and support us with a GitHub star ⭐.