# Automated Design of the Deep Neural Network Pipeline
## 4 Results
### Experiment 3--Automated Design with Transfer Learning  
#### 4.3.1 Text Processing  
Tables 12 and 13 shows the performance of SPHH when using the best=performing design from different datasets as the initial design. and thus, when using transfer learning. Table 14 presents the \(p\)-values from a statistical comparison conducted using a left-tailed Mann-Whitney U test conducted at a significance level of 0.05 (5%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Target for Design** & **Dataset Source for Design** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{Spam} & Enron & **84.70** & 0.78 & 0.77 & 0.77 \\ \cline{2-6}  & SMS Spam & **84.70** & 0.78 & 0.77 & 0.77 \\ \cline{2-6}  & YouTube & & & & \\ \cline{2-6}  & Comments & **84.70** & 0.76 & 0.75 & 0.76 \\ \cline{2-6}  & Spam & & & & \\ \cline{2-6}  & Enron & **72.45** & 0.81 & 0.62 & 0.70 \\ \cline{2-6}  & SMS Spam & 59.39 & 0.56 & 0.35 & 0.43 \\ \cline{2-6}  & Spam & & & & \\ \cline{2-6}  & assassin & 59.59 & 0.68 & 0.64 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Target** & **Dataset Source** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{2}{*}{UP} & \multirow{2}{*}{Karnataka oral lesion} & \multirow{2}{*}{89.09} & \multirow{2}{*}{0.52} & \multirow{2}{*}{0.71} & \multirow{2}{*}{0.60} \\  & & dataset & & & \\ \hline \multirow{2}{*}{ISU Rp1d} & \multirow{2}{*}{ISU V1} & \multirow{2}{*}{66.3} & \multirow{2}{*}{0.31} & \multirow{2}{*}{0.50} & \multirow{2}{*}{0.39} \\ \cline{2-6}  & & & & & \\ \hline ISU Tilt & \multirow{2}{*}{ISU V1} & \multirow{2}{*}{69.8} & \multirow{2}{*}{0.32} & \multirow{2}{*}{0.51} & \multirow{2}{*}{0.40} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Results for reusing pipeline designs created for one image processing dataset on another. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset Target** & **Dataset Source** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{6}{*}{ACL} & ACL & & & & \\  & IMDB & 95.10 & 0.87 & 0.97 & 0.92 \\  & Movie & Reviews & & & \\ \cline{2-5}  & Amazon & & & & \\  & product & 94.50 & 0.77 & 0.94 & 0.85 \\  & Review & reviews & & & \\ \cline{2-5}  & Coronavirus & **95.89** & 0.80 & 0.97 & 0.88 \\ \cline{2-5}  & Sentiment-140 & 94.22 & 0.82 & 0.85 & 0.83 \\ \cline{2-5}  & Yelp & 89.17 & 0.71 & 0.78 & 0.74 \\ \hline \multirow{6}{*}{Amazon} & ACL & & & & \\  & IMDB & & & & \\  & Movie & **86.99** & 0.74 & 0.77 & 0.75 \\ \cline{2-5}  & Reviews & & & & \\ \cline{2-5}  & Amazon & & & & \\  & product & 86.89 & 0.71 & 0.90 & 0.79 \\  & reviews & & & & \\ \cline{2-5}  & Coronavirus & 86.33 & 0.72 & 0.92 & 0.81 \\ \cline{2-5}  & Sentiment-140 & 85.94 & 0.77 & 0.93 & 0.84 \\ \cline{2-5}  & Yelp & 85.55 & 0.76 & 0.81 & 0.78 \\ \hline \multirow{6}{*}{Coronavirus} & ACL & & & & \\  & IMDB & 91.14 & 0.80 & 0.80 & 0.80 \\ \cline{2-5}  & Movie & 90.89 & 0.81 & 0.81 & 0.81 \\ \cline{2-5}  & Coronavirus & **91.55** & 0.81 & 0.80 & 0.81 \\ \cline{2-5}  & Sentiment-140 & 90.89 & 0.82 & 0.82 & 0.82 \\ \cline{2-5}  & Yelp & 90.61 & 0.80 & 0.80 & 0.80 \\ \hline \multirow{6}{*}{Sentiment-140} & ACL & & & & \\  & IMDB & 90.28 & 0.75 & 0.91 & 0.83 \\ \cline{2-5}  & Reviews & & & & \\ \cline{2-5}  & Amazon & & & & \\  & product & **91.76** & 0.81 & 0.97 & 0.88 \\ \cline{2-5}  & Coronavirus & 91.22 & 0.76 & 0.92 & 0.83 \\ \cline{2-5}  & Sentiment-140 & 91.74 & 0.79 & 0.95 & 0.86 \\ \cline{2-5}  & Yelp & 90.56 & 0.81 & 0.96 & 0.88 \\ \hline \multirow{6}{*}{Yelp} & ACL & & & & \\  & IMDB & 85.83 & 0.78 & 0.90 & 0.84 \\ \cline{2-5}  & Movie & & & & \\  & Reviews & & & & \\ \cline{2-5}  & Coronavirus & 86.67 & 0.71 & 0.78 & 0.74 \\ \cline{2-5}  & Sentiment-140 & **86.94** & 0.74 & 0.80 & 0.77 \\ \cline{2-5}  & Yelp & 86.84 & 0.74 & 0.87 & 0.80 \\ \hline \end{tabular}
\end{table} 
Table 12: Results of running the SPHH with transfer learning for the sentiment analysis datasets.