\section{Introduction}\label{sec:introduction}
\subsection{Motivation}\label{subsec:motivation}
For most supervised learning tasks lots of training samples are essential.
With too less training data the model will not generalize well and not fit a real world task.
Labeling datasets is commonly seen as an expensive task and wants to be avoided as much as possible.\cite{generalAI}
That's why there is a machine-learning field called active learning.
The general approach is to train a model that predicts within every iteration a ranking metric or Pseudo-Labels which then can be used to rank the importance of samples to be labeled by an oracle.
These labeled samples are then used to train the model.\cite{activelearning}

The goal of this practical work is to test active learning within a simple classification task and evaluate its performance.
\subsection{Research Questions}\label{subsec:research-questions}

\subsubsection{Does Active-Learning benefit the learning process?}

Should Active-learning be used for classification tasks to improve learning performance?
Furthermore, how does the sample-selection process impact the learning?
\subsubsection{Is Dagster and Label-Studio a proper tooling to build an AL Loop?}
Is combining Dagster with Label-Studio a good match for building scalable and reliable Active-Learning loops?
\subsubsection{Does balancing the learning samples improve performance?}
The sample-selection metric might select samples just from one class by chance.
Does balancing this distribution help the model performance?
\subsection{Outline}\label{subsec:outline}

In section~\ref{sec:material-and-methods} we talk about general methods and materials used.
First the problem is modeled mathematically in section~\ref{subsubsec:mathematicalmodeling} and then implemented and benchmarked in a Jupyter notebook~\ref{subsubsec:jupyternb}.
Section~\ref{sec:implementation} gives deeper insights to the implementation for the interested reader with some code snippets.
The experimental results in~\ref{sec:experimental-results} are well-presented with clear figures illustrating the performance of active learning across different sample sizes and batch sizes.
The conclusion~\ref{subsec:conclusion} provides an overview of the findings, highlighting the benefits of active learning.
Additionally the outlook section~\ref{subsec:outlook} suggests avenues for future research which are not covered in this work.