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Michael Scholz 2013-06-17 00:29:54 +02:00
parent 24c6264397
commit c29fc74efd
10 changed files with 178 additions and 33 deletions
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ss2013/1_Web Mining/Uebungen/4_Uebung/code/PorterStemmer.pyc Normal file
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ss2013/1_Web Mining/Uebungen/4_Uebung/code/naive_bayes.py
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@ -261,7 +261,7 @@ class multiclassClassifier:
result /= percentage*notwordcount
#result /= len(termfrequenciesOfClasses)
#return result
print cl +" "+str(result)
#print cl +" "+str(result)
return math.log(result)
def clean_word(self, word):
@ -321,10 +321,11 @@ if __name__ == '__main__':
listing = os.listdir(trainDir)
for classes in listing: # classes
path = trainDir+'/'+classes+'/testdata'
path = testDir
listing = os.listdir(path)
for infile in listing:
currentPath = trainDir+'/'+classes+'/testdata/'+infile
print currentPath
currentPath = testDir+'/'+infile
#print currentPath
maxRes = sys.maxint * -1
# check all possible classes
for cl in mc.percentage.keys():

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@ -51,6 +51,7 @@
\item Die gelabelten Trianinsdaten werden in zwei gleich große Mengen aufgeteilt
\item Somit erhalten wir gelabelte Trainings- und Testdaten
\item Aufteilung erfolgt zufällig
\item Somit werden keine Präferenzen gesetzt
\end{itemize}
\end{frame}
@ -59,9 +60,78 @@
\section{3. Aufgabe}
\begin{frame}
\frametitle{3. Aufgabe \\ Experimente und Abgabe}
Probleme im Naive Bayes Klassifizierer:
\begin{itemize}
\item blabla
\item Durch das Produkt über jedes Wort in einem Dokument werden Dokumente meistens großen Klassen zugeordnet.
\item Dies gilt es zu verhindern $\to$ Anpassung an Dokumentengröße
\item Trainingsmenge ist sehr klein. Um Sie zu verdoppeln wird im finalen Lauf des Klassifizierers keine Aufteilung in Test- und Trainingsmenge mehr vorgenommen.
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{3. Aufgabe \\ Experimente und Abgabe}
Ergebnisse der Validierung:
\begin{itemize}
\item Accuracy: 46.0606\%
\item Precision Macro-Avg: 41.1415\%
\item Precision Micro-Avg: 46.0606\%
\item Recall Micro-Avg: 7.8675\%
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{3. Aufgabe \\ Experimente und Abgabe}
Ergebnisse der Validierung: \\
\textbf{Precision} pro Klasse: \\
adventure: 25.0\% \\
belles\_lettres: 35.3846\% \\
editorial: 0.0\% \\
fiction: 31.25\% \\
government: 40.0\% \\
hobbies: 66.6667\% \\
learned: 72.0\% \\
lore: 20.0\% \\
mystery: 66.6667\% \\
news: 70.5882\% \\
romance: 25.0\% \\
\end{frame}
\begin{frame}
\frametitle{3. Aufgabe \\ Experimente und Abgabe}
Ergebnisse der Validierung: \\
\textbf{Recall} pro Klasse: \\
adventure: 20.0\% \\
belles\_lettres: 85.1852\% \\
editorial: 0.0\% \\
fiction: 45.4545\% \\
government: 36.3636\% \\
hobbies: 46.1538\% \\
learned: 60.0\% \\
lore: 5.5556\% \\
mystery: 50.0\% \\
news: 75.0\% \\
romance: 9.0909\% \\
\end{frame}
\begin{frame}
\frametitle{3. Aufgabe \\ Experimente und Abgabe}
Ergebnisse der Validierung: \\
\begin{figure}
\includegraphics[scale=0.45]{grafiken/a3_abb1.png}
\caption{Konfusionsmatrix Teil 1}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{3. Aufgabe \\ Experimente und Abgabe}
Ergebnisse der Validierung: \\
\begin{figure}
\includegraphics[scale=0.5]{grafiken/a3_abb2.png}
\caption{Konfusionsmatrix Teil 2}
\end{figure}
\end{frame}
\end{document}