From 894de20a5e8f55f1e87ee180a9ecd5eb92216360 Mon Sep 17 00:00:00 2001 From: Qian Cao Date: Wed, 11 Dec 2024 16:23:53 -0500 Subject: [PATCH] Update README.md --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index ba48aa8..021d0ac 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,6 @@ `calzone` is a comprehensive Python package for calculating and visualizing various metrics for assessing the calibration of models with probabilistic output. -To accurately assess the calibration of machine learning models, it is essential to have a comprehensive and reprensative dataset with sufficient coverage of the prediction space. The calibration metrics is not meaningful if the dataset is not representative of true intended population. - - ## Features - Supports multiple calibration metrics including Spiegelhalter's Z-test, Expected Calibration Error (ECE), Maximum Calibration Error (MCE), Hosmer-Lemeshow test, Cox regression analysis, and Loess regression analysis @@ -16,6 +13,8 @@ To accurately assess the calibration of machine learning models, it is essential - Prevelance adjustment to account for prevalance change between enriched data and population data. - Multiclass extension by 1-vs-rest or top-class only +To accurately assess the calibration of machine learning models, it is essential to have a comprehensive and reprensative dataset with sufficient coverage of the prediction space. The calibration metrics is not meaningful if the dataset is not representative of true intended population. + ## Installation You can install calzone using pip: