finisdhed regression chapter

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Clemens Dautermann 2020-01-22 13:06:35 +01:00
parent cf9b534127
commit 1a830d0832
9 changed files with 712 additions and 136 deletions

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@ -60,7 +60,15 @@ Das Erste Beispiel würde man als \glqq Binärklassifizierung'' bezeichnen, da z
\\ \\
Die zwei Klassen wären hier \glqq grün'' und \glqq blau''. Die Linie stellt die Klassengrenze dar, die die zwei Klassen unterscheidet. Es sind außerdem einige Ausreißer in den Daten vorhanden. Die zwei Klassen wären hier \glqq grün'' und \glqq blau''. Die Linie stellt die Klassengrenze dar, die die zwei Klassen unterscheidet. Es sind außerdem einige Ausreißer in den Daten vorhanden.
\subsection{Regressionsprobleme} \subsection{Regressionsprobleme}
Als Regressionsproblem hingegen bezeichnet man das Finden einer Funktion, die eine Menge von Eingabevariablen einer Stetigen Menge von Ausgabevariablen zuordnet. Wenn beispielsweise ein Bild eines Menschen gegeben ist, könnte ein Regressionsproblem sein, seine Höhe oder sein Gewicht zu bestimmen. Als Regressionsproblem hingegen bezeichnet man das Finden einer Funktion, die eine Menge von Eingabevariablen einer Stetigen Menge von Ausgabevariablen zuordnet. Wenn beispielsweise ein Bild eines Menschen gegeben ist, könnte ein Regressionsproblem sein, seine Höhe oder sein Gewicht zu bestimmen. Auch eine Wettervorhersage ist ein typisches Regressionsproblem. Ein Beispiel eines Regressionsproblemens wird in dieser Arbeit nicht behandelt werden. Wie Regression verbildlicht dargestellt werden kann, ist in Abbildung \ref{Regression} gezeigt.
\begin{figure}[h]
\centering
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\caption{Regression}
\label{Regression}
\end{figure}
\\
Die Kurve stellt hier Keine Grenze, sondern die Funktion, die die Werte approximiert, dar. Die Punkte repräsentieren die Eingabedaten, in denen auch hier einige Ausreißer erkennbar sind.
\subsection{Gefahren von maschinellem Lernen} \subsection{Gefahren von maschinellem Lernen}
\subsubsection{Eignung der Datensätze} \subsubsection{Eignung der Datensätze}
\subsubsection{Overfitting} \subsubsection{Overfitting}

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