## 4 Results and Discussion
### Treatment Competency: Classification and Prediction  
#### 4.2.1 Prediction Results  
This section presents the prediction results using the Pearson Correlation Coefficient (R), Mean Absolute error (MAE) and Root Mean Squared Error (RMSE). The results is reported for the total treatment competency rating and each rating item mentioned in Section 2.1. Furthermore, results from evaluating the best acoustic and linguistic features for predicting the total competency rating as well as for each rating item is presented in this section. Table 3 presents the total practitioner's competency rating prediction results using a number of different regressors for manual and automatic transcriptions. A grid search resulted in the following parameters being set for the SVR and Lasso systems, which achieved higher performance rates: SVR with a C of 0.1 and epsilon of 1.00 with linear kernel, and Lasso with alpha of 0.03. Overall, the Lasso and SVR performed better than Linear Regression, Elastic Net, Decision Tree Regressor, Ada-Boost Regressor and Gradient Boosting Regressor. The best results are achieved for when using the manual transcripts; in particular, the Lasso regressor outperform that the SVR and exhibits a higher correlation coefficient results and lower error rates. Furthermore, the manual transcriptions gained a higher correlation coefficient than the automatic ones. Based on these results, the following investigation in this section concentrates on the use of Lasso and the manual transcripts. The actual versus predicted total practitioner's competency ratings are presented in Figure 3 based on each level of competence. From the figure, it is clear that the prediction results gained better results on the medium level of competence, which is highly related to the number of occurrences of this level in the dataset. The achieved results indicate that it is feasible to use the practitioner's competency ratings as input for competency ratings prediction models, which eventually could help in assessing the quality of the therapy delivered to patients. The results of the practitioner's competency measures based on each item as defined in the manual ([10]) are displayed in Table 4. It can be seen from the results in the table that the _focusing the session_ and the _interpersonal competencies_ rating items are easier to predict with R 0.89 and 0.87, respectively. This indicates the feasibility of detecting the practitioner's empathy, which is one of the rating specifications connected to the _interpersonal competencies_ rating item. On the other hand, the _information gathering_ and the _planning and shared decision-making competencies_ rating items are harder to predict with R 0.75 and 0.73, respectively. As this is the first study to investigate the practitioner's competence, it is of interest to explore the results based on the manual transcripts, especially looking at which features could accurately describe the competence from an acoustic and linguistic perspective. In the table, the number of features output from using the RFE feature selection methods (described in Section 3.2.1) is indicated. The number of features selected to predict the