# Feature Interaction in Terms of Prediction Performance
## 3 Results
\begin{table}
\begin{tabular}{c c c} \hline
**No** & **Feature Name** & **Description** \\ \hline
1 & \
\begin{tabular}{c} Pregnant \\ Glucose \\ \end{tabular} &
\begin{tabular}{c} Number of times pregnant \\ Plasma glucose concentration (glucose tolerance test) \\ \end{tabular} \\
3 & \
\begin{tabular}{c} Pressure \\ \end{tabular} &
\begin{tabular}{c} Diastolic blood pressure (mm Hg) \\ \end{tabular} \\
4 & \
\begin{tabular}{c} Triceps \\ \end{tabular} &
\begin{tabular}{c} Triceps skin fold thickness (mm) \\ 2-Hour serum insulin (mu U/mL) \\ \end{tabular} \\
5 & \
\begin{tabular}{c} Insulin \\ \end{tabular} &
\begin{tabular}{c} 2-Hour serum insulin (mu U/mL) \\ \end{tabular} \\
6 & \
\begin{tabular}{c} Mass \\ \end{tabular} &
\begin{tabular}{c} Body mass index (weight in kg/(height in m)\({}^{2}\)) \\ \end{tabular} \\
7 & \
\begin{tabular}{c} Pedigree \\ \end{tabular} &
\begin{tabular}{c} Diabetes pedigree function \\ \end{tabular} \\
8 & \
\begin{tabular}{c} Age \\ \end{tabular} &
\begin{tabular}{c} Age (years) \\ \end{tabular} \\
9 & \
\begin{tabular}{c} Diabetes \\ \end{tabular} &
\begin{tabular}{c} Class variable (neg, pos) \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Description of the features in the PimaIndiansDiabetes dataset. \begin{table}
\begin{tabular}{c c c} \hline
**No** & **Feature Name** & **Description** \\ \hline
1 & \ \begin{tabular}{c} Crim \\ 2 \\ \end{tabular}  & \begin{tabular}{c} Per capita crime rate by town \\ Proportion of residential land zoned for lots over 25,000 sq.ft \\ \end{tabular}  \\
3 & \ \begin{tabular}{c} Indus \\ 4 \\ \end{tabular}  & \begin{tabular}{c} Proportion of nonretail business acres per town \\ Charles River dummy variable (=1 if tract bounds river; 0 otherwise) \\ \end{tabular}  \\
5 & \ \begin{tabular}{c} Nox \\ \end{tabular}  & \begin{tabular}{c} Nitric oxide concentration (parts per 10 million) \\ \end{tabular}  \\
6 & \ \begin{tabular}{c} Rm \\ \end{tabular}  & \begin{tabular}{c} Average number of rooms per dwelling \\ \end{tabular}  \\
7 & \ \begin{tabular}{c} Age \\ \end{tabular}  & \begin{tabular}{c} Proportion of owner-occupied units built prior to 1940 \\ \end{tabular}  \\
8 & \ \begin{tabular}{c} Dis \\ \end{tabular}  & \begin{tabular}{c} Weighted distances to five Boston employment centers \\ \end{tabular}  \\
9 & \ \begin{tabular}{c} Rad \\ \end{tabular}  & \begin{tabular}{c} Index of accessibility to radial highways \\ \end{tabular}  \\
10 & \ \begin{tabular}{c} Tax \\ \end{tabular}  & \begin{tabular}{c} Full-value property-tax rate per USD 10,000 \\ \end{tabular}  \\
11 & \ \begin{tabular}{c} Pratio \\ \end{tabular}  & \begin{tabular}{c} Pupil-teacher ratio by town \\ \end{tabular}  \\
12 & \ \begin{tabular}{c} B \\ \end{tabular}  & \begin{tabular}{c} _1000(B-0.63)\({}^{2}\)_, where \(B\) is the proportion of African Americans by town \\ \end{tabular}  \\
13 & \ \begin{tabular}{c} Lstat \\ \end{tabular}  & \begin{tabular}{c} Percentage of lower status of the population \\ \end{tabular}  \\
14 & \ \begin{tabular}{c} Medv \\ \end{tabular}  &
\begin{tabular}{c} Median value of owner-occupied homes in \(\$1000\  
Apart from the main implementations in this study, namely, the two-way feature interaction plots in Figures 5 and 7 and the feature interaction tables in Figures 4 and 6, several additional functions were also implemented. The following results were obtained from the BostonHousing dataset. Figure 8 shows the feature interaction details between two features, namely, \"crim\" and \"zn\". The results show that the BostonHousing dataset is based on a regression model. The total RMSE of the complete dataset is 4.679191. Excluding the \"crim\"(F1) and \"zn\"(F2) features increases the total RMSE by 0.1825532 and 0.256769, respectively. Therefore, the prediction error will increase if \"crim\" and \"zn\" are removed from the prediction model. The total RMSE increases by 0.4663873 when both the \"crim\" and \"zn\" features are excluded.