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exercise-classification: Finalized the exercise sheet itself
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dominik-probst committed Jun 5, 2024
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2 changes: 1 addition & 1 deletion exercise/3-Frequent-Patterns/Mining-Frequent-Patterns.ipynb
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0"
"version": "3.11.5"
}
},
"nbformat": 4,
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176 changes: 105 additions & 71 deletions exercise/4-Classification.tex
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\documentclass[
english,
smallborders
Expand Down Expand Up @@ -41,9 +42,9 @@ \section*{About this Exercise Sheet}

This exercise sheet focuses on the content of lecture \textit{7. Classification}.

It includes TODO.
It includes both theoretical exercises on Decision Trees (Exercise 1) and Naïve Bayes (Exercise 2) and a practical data science exercise (Exercise 3).

The exercise sheet is designed for a three-week period, during which the tasks can be completed flexibly.
The exercise sheet is designed for a three-week period, during which the tasks can be completed flexibly (Planned is one exercise per week).

The sample solution will be published after the three weeks have elapsed.

Expand Down Expand Up @@ -77,22 +78,29 @@ \section*{Preparation}

\section*{Exercise 1: Decision Trees}

Given is a dataset $D$:

\begin{center}
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{Age} & \textbf{Major} & \textbf{Participation} & \textbf{Passed} \\ \hline
23 & CS & High & Yes \\ \hline
23 & DS & Low & No \\ \hline
26 & DS & High & Yes \\ \hline
24 & DS & Medium & Yes \\ \hline
26 & DS & Medium & No \\ \hline
26 & DS & Low & No \\ \hline
\end{tabular}
\end{center}

$D$ is containing a continuous attribute (\textit{Age}) and two categorical attributes (\textit{Major} and \textit{Participation}) which can be used to predict the target attribute \textit{Passed}.
\begin{minipage}{.5\textwidth}
Given is a dataset $D$.

\vspace*{0.5cm}

$D$ is containing a continuous attribute (\textit{Age}) and two categorical attributes (\textit{Major} and \textit{Participation}) which can be used to predict the target attribute \textit{Passed}.
\end{minipage}
\begin{minipage}{.5\textwidth}
\begin{flushright}
\scalebox{0.85}{
\begin{tabular}{|c|c|c|c|c|c|}
\hline
\textbf{Age} & \textbf{Major} & \textbf{Participation} & \textbf{Passed} \\ \hline
23 & CS & High & Yes \\ \hline
23 & DS & Low & No \\ \hline
26 & DS & High & Yes \\ \hline
24 & DS & Medium & Yes \\ \hline
26 & DS & Medium & No \\ \hline
26 & DS & Low & No \\ \hline
\end{tabular}
}
\end{flushright}
\end{minipage}

\subsection*{Task 1: Information Gain}

Expand Down Expand Up @@ -911,45 +919,61 @@ \subsection*{Task 3: Gain Ratio}

\section*{Exercise 2: Naïve Bayes}

Given is a dataset $D$:

\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
% Basic Idea: Submission Topic & Prior Knowledge & Hours Invested & Passed
\textbf{Topic} & \textbf{Knowledge} & \textbf{Hours} & \textbf{Passed} \\ \hline
Classification & High & 1,0 & No \\ \hline
Clustering & Low & 4,0 & No \\ \hline
Frequent Patterns & High & 5,0 & Yes \\ \hline
Clustering & Medium & 5,0 & Yes \\ \hline
Frequent Patterns & High & 2,0 & No \\ \hline
Frequent Patterns & Medium & 3,0 & Yes \\ \hline
Classification & Low & 6,0 & Yes \\ \hline
Clustering & Low & 5,0 & Yes \\ \hline
Clustering & High & 3,0 & Yes \\ \hline
Classification & Medium & 4,0 & Yes \\ \hline
\end{tabular}
\end{center}

It can be assumed that \textit{Topic}, \textit{Knowledge} and \textit{Hours} are conditionally independent of each other.

The attributes \textit{Topic} and \textit{Knowledge} are categorical attributes. \newline
The attribute \textit{Hours} is a continuous attribute. It can be assumed that the values of this attribute are distributed according to a Gaussian distribution.
\begin{minipage}{.375\textwidth}
Given is a dataset $D$.

\vspace*{0.5cm}

It can be assumed that \textit{Topic}, \textit{Knowledge} and \textit{Hours} are conditionally independent of each other.

\vspace*{0.5cm}

The attributes \textit{Topic} and \textit{Knowledge} are categorical attributes.

\vspace*{0.1cm}

The attribute \textit{Hours} is a continuous attribute. It can be assumed that the values of this attribute are distributed according to a Gaussian distribution.
\end{minipage}
\begin{minipage}{.625\textwidth}
\begin{flushright}
\scalebox{0.85}{
\begin{tabular}{|c|c|c|c|}
\hline
% Basic Idea: Submission Topic & Prior Knowledge & Hours Invested & Passed
\textbf{Topic} & \textbf{Knowledge} & \textbf{Hours} & \textbf{Passed} \\ \hline
Classification & High & 1,0 & No \\ \hline
Clustering & Low & 4,0 & No \\ \hline
Frequent Patterns & High & 5,0 & Yes \\ \hline
Clustering & Medium & 5,0 & Yes \\ \hline
Frequent Patterns & High & 2,0 & No \\ \hline
Frequent Patterns & Medium & 3,0 & Yes \\ \hline
Classification & Low & 6,0 & Yes \\ \hline
Clustering & Low & 5,0 & Yes \\ \hline
Clustering & High & 3,0 & Yes \\ \hline
Classification & Medium & 4,0 & Yes \\ \hline
\end{tabular}
}
\end{flushright}
\end{minipage}

\subsection*{Task 1: Classification}


Use the dataset $D$ and the Naïve Bayes algorithm to classify the following tuples:

\begin{center}
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Topic} & \textbf{Knowledge} & \textbf{Hours} & \textbf{Passed} \\ \hline
Clustering & Medium & 4,0 & ? \\ \hline
Classification & High & 3,0 & ? \\ \hline
Frequent Patterns & Low & 6,8 & ? \\ \hline
\end{tabular}
\scalebox{0.85}{
\begin{tabular}{|c|c|c|c|}
\hline
\textbf{Topic} & \textbf{Knowledge} & \textbf{Hours} & \textbf{Passed} \\ \hline
Clustering & Medium & 4,0 & ? \\ \hline
Classification & High & 3,0 & ? \\ \hline
Frequent Patterns & Low & 6,8 & ? \\ \hline
\end{tabular}
}
\end{center}


Write down \textbf{all} intermediate steps.

\begin{solution}
Expand Down Expand Up @@ -1153,33 +1177,32 @@ \subsection*{Task 1: Classification}
\end{enumerate}
\end{solution}

\newpage

\subsection*{Task 2: Model Evaluation}

The classifier was also trained on a version of dataset $D$ with more tuples.
The classifier was also trained on a version of dataset $D$ with more tuples:

To test the quality of the resulting model, some test values were classified.

The dataset $T$ contains both the true and the predicted "Passed"-Status for each test tuple:
The dataset $T$ contains both the true and the predicted "Passed"-Status for each test tuple.

\begin{center}
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Topic} & \textbf{Knowledge} & \textbf{Hours} & \textbf{\begin{tabular}[c]{@{}c@{}}Passed\\ (True)\end{tabular}} & \textbf{\begin{tabular}[c]{@{}c@{}}Passed\\ (Pred)\end{tabular}} \\ \hline
Classification & Medium & 7,5 & Yes & Yes \\ \hline
Frequent Patterns & Low & 1,8 & No & No \\ \hline
Frequent Patterns & High & 3,7 & No & Yes \\ \hline
Frequent Patterns & Low & 0,2 & No & No \\ \hline
Frequent Patterns & High & 1,4 & Yes & No \\ \hline
Frequent Patterns & High & 9,9 & Yes & Yes \\ \hline
Frequent Patterns & Medium & 7,3 & Yes & Yes \\ \hline
Frequent Patterns & Low & 4,3 & No & Yes \\ \hline
Classification & Medium & 5,5 & Yes & Yes \\ \hline
Clustering & Low & 0,1 & No & No \\ \hline
\end{tabular}
\scalebox{0.85}{
\begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Topic} & \textbf{Knowledge} & \textbf{Hours} & \textbf{\begin{tabular}[c]{@{}c@{}}Passed\\ (True)\end{tabular}} & \textbf{\begin{tabular}[c]{@{}c@{}}Passed\\ (Pred)\end{tabular}} \\ \hline
Classification & Medium & 7,5 & Yes & Yes \\ \hline
Frequent Patterns & Low & 1,8 & No & No \\ \hline
Frequent Patterns & High & 3,7 & No & Yes \\ \hline
Frequent Patterns & Low & 0,2 & No & No \\ \hline
Frequent Patterns & High & 1,4 & Yes & No \\ \hline
Frequent Patterns & High & 9,9 & Yes & Yes \\ \hline
Frequent Patterns & Medium & 7,3 & Yes & Yes \\ \hline
Frequent Patterns & Low & 4,3 & No & Yes \\ \hline
Classification & Medium & 5,5 & Yes & Yes \\ \hline
Clustering & Low & 0,1 & No & No \\ \hline
\end{tabular}
}
\end{center}


Use the dataset $T$ to calculate the \textbf{sensitivity}, \textbf{specificity}, \textbf{accuracy}, \textbf{precision}, \textbf{recall}, and \textbf{F1-score} of the model.

Also state the \textbf{best possible} value for each metric.
Expand Down Expand Up @@ -1248,9 +1271,20 @@ \subsection*{Task 2: Model Evaluation}

\newpage

\section*{Exercise 3: TODO}
\section*{Exercise 3: Conducting Classification}

This exercise comprises practical data science tasks and thus utilizes a Jupyter Notebook:

TODO
\begin{enumerate}
\item Open \texttt{Conducting-Classification.ipynb}.
\item Take a look at the tasks (blue boxes) in the notebook and try to solve them.
\end{enumerate}

If you are unfamiliar with how to open a Jupyter Notebook, please refer to Exercise 1 of \texttt{1-Introduction-Python-Pandas.pdf}.

\begin{solution}
The solution to the exercise can be found in \texttt{Additional-Files-Solution.zip}.
\end{solution}


\end{document}

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