From ae413f9183a040b537002a0662ee50f24aec9119 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Alexander=20M=C3=A4rz?= <alex.maerz@gmx.net>
Date: Tue, 23 May 2023 17:26:13 +0200
Subject: [PATCH] Update to release 0.2.0

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 6581e73..7a5e431 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ We propose a new framework of LightGBM that predicts the entire conditional dist
 :white_check_mark: Automatic derivation of Gradients and Hessian of all distributional parameters using [PyTorch](https://pytorch.org/docs/stable/autograd.html). <br/>
 :white_check_mark: Automated hyper-parameter search, including pruning, is done via [Optuna](https://optuna.org/). <br/>
 :white_check_mark: The output of LightGBMLSS is explained using [SHapley Additive exPlanations](https://github.com/dsgibbons/shap). <br/>
-:white_check_mark: LightGBMLSS provides full compatibility with all the features and functionality of XGBoost. <br/>
+:white_check_mark: LightGBMLSS provides full compatibility with all the features and functionality of LightGBM. <br/>
 :white_check_mark: LightGBMLSS is available in Python. <br/>
 
 ## News