## 3 Results  
The selection of models for inclusion in the AutoML approach is first determined relative to a baseline RMSE score. While this baseline model is not expected to perform well, it is performed to provide an error score that the score of more complex models can be compared to. A baseline score of RMSE 20.22 was established by calculating the error when the mean TDT score is used as a prediction for every case; this score is represented by a red line in Figure 2.  
LightGBM, Xgboost, CatBoost, neural network, and random forest models were trained and evaluated. No linear models passed the requirements of the exploratory stage of MLjar and were not included in the models trained in the 'perform' stage. A comparison between the RMSE score achieved, grouped by model type, can be seen in Figure 2. These results show that ANN performed poorly, or at least inconsistently, compared to other model architectures and often performed worse than baseline (RMSE 20.22). This is not surprising given the limited amount of data for training, as neural networks are known to have poor performance without a large amount of training data. Light gradient boosting machine (LightGBM) performed consistently well, to the point where an attempt at creating an ensemble, by combining high-performing trained models, did no better than a single LightGBM model with feature selection and golden features achieved on its own. The four most important features across the five folds for the best-performing LightGBM model with golden feature, selected features, and hill climbing steps applied (23_LightGBM_GF_SF_HC1) can be seen in Figure 3. The two features with the highest mean importance scores were the derived 'golden features'. These relationships appeared across all of the folds of the cross-validation, providing evidence of the robustness of the relationship between these correlations and the TDT score. Thus, the process of creating derived or golden features found combinations that performed well and warrant further exploration for their efficacy in directing rehabilitation efforts and the prediction of likely recovery outcomes. The golden feature with the highest mean feature importance (0.36) involved a difference in correlation between the right insula and right superior temporal region (Insula_R <=> Temporal_Sup_R) and correlation between area 3 of the left cerebellum and vermis (Cerebelum_3_L  
Figure 2: Model performance comparison. <=> Vermis_1_2). The golden feature with the second highest mean feature importance (0.21) was based on the ratio of the correlation between the right inferior temporal lobe and left cerebellum (Temporal_Inf_R <=> Cerebelum_7b_L) and the left frontal inferior operculum and left supplementary motor area (Frontal_Inf_Oper_L <=> Supp_Motor_Area_L). These golden features contain information that are valuable predictors of the achieved TDT score across the majority of the group. These interactions between correlations were more valuable predictors of TDT scores than any other single correlation. Figure 3: Feature importance across the five learners, which correspond to the five folds of the cross-validation, in the best-performing LightGBM model with golden features, selected features, and hill climbing steps applied (23_LightGBM_GF_SF_HC1). A table of all of the models trained, training steps involved, and RMSE achieved can be seen in Table 2. Table 2 compares various machine learning models based on their RMSE scores. Models are organised by type and the optimisation strategies applied, showcasing their performance across different enhancement steps. An asterisk (*) indicates the model with the lowest RMSE in each step. Numbers at the start of the configuration name are used to differentiate new models of the same type from previous models that performed well and have extra steps applied, eg., 1_Default_LightGBM and 1_Default_LightGBM_GF_SF (the same model with golden features and selected features). The specific hyperparameters for each model can be seen in Appendix A.