# Prediction of Eudaimonic and Hedonic Orientation of Movie Watchers
## 4 Results and Discussion
### Eudaimonic and Hedonic Orientation Prediction
#### 4.2.1 Regression  
We evaluated seven machine-learning-regression models. As the baseline, we used the predictor of the mean value of the target variable. We considered two feature sets in the manual feature selection step: (i) U-F features, which contain user-related features (except for EHOQ and EHO variables, which are the target variables) and (ii) I-F features, which describe the users' interactions. The detailed list of features is reported in Table 1.  
To evaluate the prediction of the various algorithms we used the mean absolute error (MAE) and the root mean squared error (RMSE), which penalizes large errors more than MAE [32]. The results are reported in Tables 4-6, for feature sets U-F, I-F and both, respectively.  
Figure 6: The different number of clusters using the _silhouette method_ over eudaimonic and hedonic variables. (**a**): k = 2, (**b**): k = 3, (**c**): k = 4 and (**d**): k = 5. Parameter \(k\) determines the number of clusters. The average value of the Silhouette coefficients is shown with a red dashed line.  
In the regression problem, in the case of using U-F features (Table 4), we can see that Ridge and Lasso performed better compared to the baseline. The SVR also performed better than the baseline based on the MAE and RMSE value in predicting the eudaimonic orientation of users. In terms of hedonic orientation prediction by SVR, only the RMSE value is lower than the baseline. Random Forest and XGBoost provided slightly lower  
\begin{table}
\begin{tabular}{c c c c c} \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 (0.07) & 0.48 (0.07) & 1.02 (0.07) & 0.86 (0.10) \\ \hline Lasso & 0.88 (0.06) & 0.48 (0.07) & 1.02 (0.07) & 0.86 (0.11) \\ \hline SVR & 0.96 (0.10) & 0.66 (0.11) & 1.10 (0.09) & 1.17 (0.17) \\ \hline KNN & 1.07 (0.09) & 1.01 (0.15) & 1.11 (0.08) & 1.14 (0.16) \\ \hline Decision Tree & 1.09 (0.10) & 1.06 (0.15) & 1.15 (0.08) & 1.37 (0.21) \\ \hline Random Forest & 1.07 (0.09) & 1.00 (0.17) & 1.09 (0.11) & 1.09 (0.19) \\ \hline XGBoost & 1.06 (0.15) & 0.98 (0.26) & 1.08 (0.07) & 1.05 (0.13) \\ \hline \end{tabular}
\end{table}
Table 4: EHO prediction results (regression problem) using U-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.  
Figure 7: The different number of clusters using _silhouette method_ over eudaimonic and hedonic variables. (**a**): number of clusters = 2 and (**b**): number of clusters = 3. Parameter _n_clusters_ determines the number of clusters. The average value of the Silhouette coefficients is shown with a red dashed line.  
values of RMSE than the baseline but higher MAE values. The decision tree algorithm was only better at predicting hedonic orientation based on the RMSE metric than other methods.  
In the regression problem, in the case of using I-F features (Table 5), we can see that the results are more or less close to the baseline, whereas using both U-F and I-F features (Table 6) results are better, except for the MAE value for the KNN algorithm, which is not better but still close to the baseline.