\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 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 we use.
First the problem is modeled mathematically in~\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.
The conclusion~\ref{subsec:conclusion} provides a overview of the findings, highlighting the benefits of active learning.
Additionally the outlook section suggests avenues for future research which are not covered in this work.
The experimental results are well-presented with clear figures illustrating the performance of active learning across different sample sizes and batch sizes.

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