\begin{slide}{Eg1: Text Mining \@ NASA }
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\item
		901 NASA records, PITS issue tracker:  \{severity, free text\}
\ei

~\\

{\footnotesize
			\begin{tabular}{l|r@{~}l}
			severity & frequency& \\\hline
			1 (panic)        & 0 &\\
			2        & 311 &\rule{20mm}{2mm}\\
			3        & 356 &\rule{22mm}{2mm}\\
			4        & 208 & \rule{16mm}{2mm}\\
			5 (yawn)       & 26  & \rule{1mm}{2mm}
			\end{tabular}}
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\item
		Top 100 unique words (selected by  Tf*IDF); sorted by InfoGain
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		Rules learned from  N best (RIPPER~\cite{cohen95} 
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}{
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\item
		Severity 2 predictors:\newline 
10*\{(train,test) = (90,10)\%\}
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{\scriptsize		\begin{tabular}{r|rr|l}
	N & a=recall  & b=precision& $F=\frac{2*a*b}{a+b}$\\\hline
		100      &    0.81                            &0.93    & 0.87  \\
		50       &    0.80                           & 0.90    & 0.85  \\
		25       &    0.79                           & 0.93    & 0.85  \\
		12       &    0.74                              & 0.92  & 0.82  \\
		6        &    0.71                           & 0.94    & 0.81  \\
		3        &    0.74                           & 0.82    & 0.78 \\\hline 
		\end{tabular}}

~\\

Rules (from N=3 words):

{\scriptsize	\begin{tabular}{r@{~}l@{~}r}
            & if (rvm $\le$ 0) \& (srs = 3) &$\rightarrow$ sev=4\\
        else &if (srs $\ge$ 2)              &$\rightarrow$ sev=2 \\
        else  &                         & $\rightarrow$ sev=3\\
		\end{tabular}}
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\item
	Diamonds in the dust
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\item
		Not  9414 words total 
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or 1662 unique words
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 but 3 highly predictive words
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}
\end{slide}
\begin{slide}[method=file,toc=Eg1 (more)]{Top 3  words }
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\item
In issue reports from four other projects:
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\item
10*\{(train,test) = (90,10)\%\}
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yields probability of detection of highest severity class of 93\% to 99.8\%.
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\item (Note: ignoring real rare classes.)
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~\\

\begin{minipage}{.5\linewidth}
Project ``b'': 984 records
{\scriptsize \begin{verbatim}
a   b   c   d   <-- classified as
1 380   0   0 |   a = _4
1 520   0   0 |   b = _3 pd=99.8%
0  59   0   0 |   c = _5
0  23   0   0 |   d = 2\end{verbatim}
} 
\end{minipage}
\begin{minipage}{.45\linewidth}
Project ``d'': 180 records
{\scriptsize\begin{verbatim}
  a   b   c   <-- classified as
157  23   0 |   a = _4
  9 121   0 |   b = _3 pd=99.4%
  6   1   0 |   c = _5 

\end{verbatim} 
  

} 
\end{minipage}

\vspace{0.5cm}

\begin{minipage}{.5\linewidth}
Project ``c'': 317 records
{\scriptsize\begin{verbatim}
  a   b   c   <-- classified as
  9 121   0 |   b = _3 pd=93.1%
157  23   0 |   a = _4
  6   1   0 |   c = _5 

\end{verbatim} 
} 
\end{minipage}
\begin{minipage}{.45\linewidth}
Project ``e'': 832 records
{\scriptsize\begin{verbatim}
a   b   c   d   <-- classified as
0  23   0   0 |   a = _2
0 498   0  18 |   b = _3 pd=96.5%
0  34   0   7 |   c = _5
0 178   0  65 |   d = _4 \end{verbatim} 
}
\end{minipage}
\end{slide}

