diff --git a/report_thesis/src/sections/background.tex b/report_thesis/src/sections/background.tex
index d29e4d83..ccb912e0 100644
--- a/report_thesis/src/sections/background.tex
+++ b/report_thesis/src/sections/background.tex
@@ -320,7 +320,7 @@ \subsubsection{Gradient Boosting Regression (GBR)}\label{sec:gradientboost}
 In this section we introduce \gls{gbr} primarily based on \citet{James2023AnIS}.
 \gls{gbr} is an ensemble learning method that builds models sequentially, each one trying to correct the errors of the previous one, using gradient descent and boosting techniques.
 
-However, in order to explain \gls{gbr}, it is helpful to build on the concepts of ensemble learning and decision trees.
+To understand \gls{gbr}, it is helpful to build on the concepts of ensemble learning and decision trees.
 Ensemble learning is a technique in machine learning where multiple models, known as \textit{weak learners}, are combined to produce more accurate predictions.
 Mathematically, ensemble learning can be defined as combining the predictions of $M$ weak learners to form a final prediction $\hat{y}$, such that:
 \begin{equation}