# Prediction of Eudaimonic and Hedonic Orientation of Movie Watchers
## 4 Results and Discussion
### Eudaimonic and Hedonic Orientation Prediction
#### 4.2.2 Class Prediction
Classification problems on two classes: (a): High_EO (High_Eudaimonic Orientated) and (b): Low_EO (Low Eudaimonic Oriented). Numbers are the mean value of 10 outer splits. Numbers in parenthesis indicate the standard deviation values. \begin{table}
\begin{tabular}{c c c c c c} \hline
**ML Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **ROC AUC** \\ \hline Base & 0.56 (0.00) & 0.00 (0.00) & 0.00 (0.00) & 0.00 (0.00) & 0.50 (0.00) \\ \hline Ridge & 0.66 (0.07) & 0.63 (0.10) & 0.63 (0.11) & 0.62 (0.05) & 0.72 (0.07) \\ \hline SVC & 0.65 (0.07) & 0.60 (0.13) & 0.61 (0.15) & 0.60 (0.12) & 0.69 (0.10) \\ \hline KNN & 0.59 (0.08) & 0.55 (0.15) & 0.48 (0.08) & 0.51 (0.10) & 0.60 (0.09) \\ \hline Decision Tree & 0.53 (0.11) & 0.53 (0.13) & 0.53 (0.13) & 0.52 (0.12) & 0.52 (0.11) \\ \hline Random Forest & 0.63 (0.06) & 0.63 (0.15) & 0.52 (0.12) & 0.54 (0.10) & 0.65 (0.06) \\ \hline \end{tabular}
\end{table} 
Table 8: HO prediction results (classification problem) using U-F features. Nested cross-validation with 10 outer and five inner splits. Classification problems on two classes: (a): High_HO (High_Hedonic Orientated) and (b): Low_HO (Low Hedonic Oriented). Numbers are the mean value of 10 outer splits. Numbers in parenthesis indicate the standard deviation values. \begin{table}
\begin{tabular}{c c c c c c} \hline
**ML Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **ROC AUC** \\ \hline Base & 0.51 (0.00) & 0.00 (0.00) & 0.00 (0.00) & 0.00 (0.00) & 0.50 (0.00) \\ \hline Ridge & 0.56 (0.02) & 0.56 (0.04) & 0.54 (0.04) & 0.55 (0.01) & 0.59 (0.02) \\ \hline SVC & 0.60 (0.03) & 0.60 (0.03) & 0.58 (0.05) & 0.59 (0.02) & 0.63 (0.03) \\ \hline KNN & 0.57 (0.04) & 0.57 (0.04) & 0.55 (0.05) & 0.56 (0.04) & 0.60 (0.04) \\ \hline Decision Tree & 0.62 (0.04) & 0.61 (0.05) & 0.61 (0.06) & 0.61 (0.05) & 0.64 (0.06) \\ \hline Random Forest & 0.61 (0.06) & 0.60 (0.06) & 0.61 (0.06) & 0.60 (0.05) & 0.66 (0.07) \\ \hline \end{tabular}
\end{table} 
Table 9: EO prediction results (classification problem) using I-F features. Nested cross-validation with 10 outer and five inner splits. Classification problems on two classes: (a): High_EO (High_Eudaimonic Orientated) and (b): Low_EO (Low Eudaimonic Oriented). Numbers are the mean value of 10 outer splits. Numbers in parenthesis indicate the standard deviation values. For most machine-learning algorithms, the results of classification problems using I-F features or U-F features are better than those using the baseline in both cases. When I-F features are used, the decision tree provides better results. With other machine-learning algorithms, the results are close or better than the baseline, regardless of whether I-F or U-F features are used. The baseline performed better in terms of accuracy only when using the decision tree model to predict HO classes using U-F features. For both EO and HO class prediction (Tables 11 and 12), the Ridge as well as Random Forest algorithms outperformed the baseline significantly. \begin{table}
\begin{tabular}{c c c c c c} \hline
**ML Algorithm** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **ROC AUC** \\ \hline Base & 0.56 (0.00) & 0.00 (0.00) & 0.00 (0.00) & 0.00 (0.00) & 0.50 (0.00) \\ \hline Ridge & 0.65 (0.06) & 0.62 (0.14) & 0.60 (0.09) & 0.59 (0.07) & 0.70 (0.07) \\ \hline SVC & 0.55 (0.07) & 0.50 (0.15) & 0.46 (0.08) & 0.47 (0.09) & 0.56 (0.08) \\ \hline KNN & 0.54 (0.09) & 0.49 (0.16) & 0.49 (0.14) & 0.48 (0.13) & 0.54 (0.10) \\ \hline Decision Tree & 0.61 (0.06) & 0.58 (0.16) & 0.59 (0.11) & 0.56 (0.07) & 0.62 (0.07) \\ \hline Random Forest & 0.64 (0.07) & 0.61 (0.18) & 0.53 (0.11) & 0.56 (0.12) & 0.68 (0.06) \\ \hline \end{tabular}
\end{table} 
Table 10: HO prediction results (classification problem) using I-F features. Nested cross-validation with 10 outer and five inner splits. Classification problems on two classes: (a): High_HO (High Hedonic Orientated) and (b): Low_HO (Low Hedonic Oriented). Numbers are the mean value of 10 outer splits.