## 4 Results and Discussion
### Treatment Competency: Classification and Prediction
#### 4.2.1 Prediction Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Regressor**} & \multicolumn{4}{c}{**Automatic Transcripts**} & \multicolumn{4}{c}{**Manual Transcripts**} \\ \cline{2-7}  & **MAE** & **RMSE** & **R** & **MAE** & **RMSE** & **R** \\ \hline Lasso & 1.91 (3.76\%) & 2.64 (5.72\%) & 0.88 & **1.66 (3.25\%)** & **2.25 (4.42\%)** & **0.92** \\ SVR & 2.91 (5.72\%) & 3.67 (7.19\%) & 0.84 & 2.65 (5.20\%) & 3.12 (6.11\%) & 0.91 \\ Elastic Net & 3.51 (6.88\%) & 4.30 (8.43\%) & 0.76 & 3.39 (6.64\%) & 4.24 (8.31\%) & 0.77 \\ Linear Regression & 3.98 (7.80\%) & 4.91 (9.62\%) & 0.60 & 3.80 (7.45\%) & 4.59 (9.00\%) & 0.69 \\ Decision Tree & 3.86 (7.56\%) & 6.24 (12.23\%) & 0.45 & 3.61 (7.07\%) & 5.39 (10.56\%) & 0.51 \\ Ada-Boost & 3.54 (6.94\%) & 5.53 (10.84\%) & 0.45 & 3.50 (6.86\%) & 5.25 (10.29\%) & 0.47 \\ Gradient Boosting & 4.31 (8.45\%) & 5.77 (11.31\%) & 0.44 & 3.51 (6.88\%) & 4.80 (9.41\%) & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The total practitioner’s competency rating prediction results using several regressors for manual and automatic transcriptions (the MAE and RMSE percentages of the total dataset). continued engagement competencies_ rating item is the lowest number of features in comparison to the other items. In general, applying the RFE method revealed the best, selected acoustic and linguistic features for predicting the total practitioner's competency measure, as presented in Figure 4 for the patient's (PAT) and practitioner's (INT) speech. As shown in the figure, the best eGeMAPS features for the patient is the standard deviation of the third formant frequency and this feature could reflect on the vowel pronunciation in the patient's speech. For the practitioner, the mean of the third MFCC coefficient has been selected as the most optimal feature for predicting the competency, which could reflect on the practitioner's voice timbre [56]. The best linguistic feature for the patient is the MHigh of WRAD, which relates to the patient being in the RA symbolising phase, verbally immersed in a memory or a dream. For the practitioner, the best linguistic features are the WRRL MHigh, the AP and AS covariation, the AZ and WRSL covariation and the WRAD and WRSL covariation. The former best features can be divided into two groups based on the practitioner's levels of competence: the high competency practitioners would be expected to mainly reflect on the patient's mental experience, which is captured by WRRL MHigh, with a positive effect showing a range of emotions, which is captured by AP and AS covariation. Low to medium competency practitioners express with a natural affect language (AZ) neither positive nor negative located either in the arousal phase of the referential process (WRSL) or in the symbolising phase (WRAD), which means that the practitioner in those phases is not reflecting or engaging with the patient and this is indicated by the low to medium ratings of competency. Measuring the covariation between those dictionaries could capture the low to medium level of practitioner's competence, as found in the selected features by the prediction model. It is more difficult to provide an explanation for the best BoAWs features, because each selected word corresponds to a combination of MFCC acoustic features. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Competency Items** & **Num. Feat.** & **MAE** & **RMSE** & **R** \\ \hline
1—Focusing the session & 27/520 & 0.35 (6.86\%) & 0.44 (8.75\%) & 0.89 \\
2—Continued engagement competencies & 11/250 & 0.30 (6.02\%) & 0.38 (7.55\%) & 0.82 \\
3—Interpersonal competencies & 23/250 & 0.34 (6.76\%) & 0.42 (8.39\%) & 0.87 \\
4—Information gathering: specific to change & 33/250 & 0.51 (10.17\%) & 0.62 (12.31\%) & 0.75 \\
5—Within session self-help change method & 27/250 & 0.33 (6.64\%) & 0.41 (8.16\%) & 0.81 \\
6—Planning and shared decision-making competencies & 24/250 & 0.34 (6.70\%) & 0.45 (8.98\%) & 0.73 \\ \hline \hline \end{tabular}
\end{table}