# Prediction of Eudaimonic and Hedonic Orientation of Movie Watchers
## 4 Results and Discussion
### Eudaimonic and Hedonic Orientation Prediction
#### 4.2.2 Class Prediction  
For the classification problem, the target variables were converted to classes using median splitting. In order to evaluate the classifiers, we used the accuracy, precision, recall, F1 score and the area under the curve (AUC) in the receiver operating characteristic (ROC). Again, we compared U-F and I-F features. As the baseline, we used the majority classifier. Since we used median splitting, the metrics values should be 0.5; however, due to slight inequalities caused by data splitting, they are slightly off the 0.5 value. When using the U-F features (Tables 7 and 8), the Ridge, SVC and Random Forest algorithms performed better than the baseline. The decision tree and KNN performed close but still better than the baseline. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ML Algorithm** & **RMSE (EO)** & **MAE (EO)** & **RMSE (HO)** & **MAE (HO)** \\ \hline Base & 1.09 (0.00) & 0.85 (0.00) & 1.24 (0.00) & 0.97 (0.00) \\ \hline Ridge & 0.88 (0.04) & 0.48 (0.05) & 1.03 (0.04) & 0.87 (0.07) \\ \hline Lasso & 0.87 (0.05) & 0.47 (0.05) & 1.02 (0.05) & 0.86 (0.07) \\ \hline SVR & 0.97 (0.07) & 0.69 (0.06) & 1.02 (0.05) & 0.86 (0.08) \\ \hline KNN & 1.04 (0.08) & 0.90 (0.10) & 1.05 (0.04) & 0.95 (0.08) \\ \hline Decision Tree & 1.01 (0.07) & 0.82 (0.08) & 1.05 (0.06) & 0.93 (0.09) \\ \hline Random Forest & 1.01 (0.06) & 0.82 (0.08) & 1.04 (0.07) & 0.90 (0.11) \\ \hline XGBost & 0.98 (0.04) & 0.74 (0.06) & 1.04 (0.07) & 0.89 (0.10) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: EHO prediction results (regression problem) using U-F and I-F features. Nested cross-validation with 10 outer and five inner splits. EO (Eudaimonic orientation) and HO (Hedonic orientation). 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} \hline \hline
**ML Algorithm** & **RMSE (EO)** & **MAE (EO)** & **RMSE (HO)** & **MAE (HO)** \\ \hline Base & 1.09 (0.00) & 0.85 (0.00) & 1.24 (0.00) & 0.97 (0.00) \\ \hline Ridge & 1.02 (0.09) & 0.87 (0.12) & 1.04 (0.08) & 0.94 (0.12) \\ \hline Lasso & 1.02 (0.09) & 0.86 (0.12) & 1.05 (0.07) & 0.94 (0.11) \\ \hline SVR & 1.04 (0.09) & 0.92 (0.13) & 1.07 (0.09) & 1.01 (0.15) \\ \hline KNN & 1.05 (0.10) & 0.95 (0.14) & 1.10 (0.10) & 1.16 (0.18) \\ \hline Decision Tree & 1.08 (0.11) & 1.06 (0.18) & 1.10 (0.08) & 1.15 (0.15) \\ \hline Random Forest & 1.06 (0.11) & 0.99 (0.15) & 1.10 (0.09) & 1.15 (0.17) \\ \hline XGBoost & 1.07 (0.12) & 1.03 (0.19) & 1.09 (0.09) & 1.09 (0.16) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: EHO prediction results (regression problem) using I-F features. Nested cross-validation with 10 outer and five inner splits. EO (Eudaimonic orientation) and HO (Hedonic orientation). Numbers are the mean value of 10 outer splits. Numbers in parenthesis indicate the standard deviation values. In the classification problem of predicting EO and HO classes using I-F features (Tables 9 and 10), the Ridge, SVC and Random Forest algorithms performed better than the baseline. The KNN performed close but still better than the baseline. We can see that the decision tree performed better in predicting HO classes than in predicting EO classes. \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 & 0.78 (0.08) & 0.75 (0.12) & 0.86 (0.12) & 0.79 (0.08) & 0.89 (0.07) \\ \hline SVC & 0.65 (0.07) & 0.66 (0.09) & 0.61 (0.12) & 0.63 (0.08) & 0.71 (0.08) \\ \hline KNN & 0.57 (0.11) & 0.57 (0.15) & 0.55 (0.15) & 0.55 (0.13) & 0.60 (0.10) \\ \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 & 0.62 (0.08) & 0.62 (0.09) & 0.58 (0.09) & 0.60 (0.07) & 0.68 (0.08) \\ \hline \end{tabular}
\end{table} 
Table 7: EO prediction results (classification problem) using U-F features. Nested cross-validation with 10 outer and five inner splits.