2 38 CHAPTER 2 The accuracy and F1-score of the individual algorithms differ. Figure 3 visualizes the results of the average of the individual scores. Figure 3. Average accuracy and F1-score per model. For thirty-five subjects, the best-performing individual model was the Random Forest algorithm, in eight cases this was the Decision Tree algorithm, and for one subject the AdaBoost algorithm performed best. The average accuracy of the Random Forest algorithm is 0.93 (range 0.88–0.99). Thus, in terms of accuracy, the individual Random Forest models score better than its counterpart that was generalized over all individuals (mean personalized accuracy = 0.93 versus mean generalized accuracy = 0.82). The average accuracy of the Decision Tree model is 0.93 (range 0.91–0.97) and outperforms the generalized, group-based Decision Tree accuracy of 0.75. The accuracy of the single AdaBoost model is 0.98, which outperforms the group accuracy of 0.85. The mean F1-score of the Random Forest model is 0.90 (range 0.87–0.94). The mean F1score of the Decision Tree model based on the eight best performing models is 0.90 (range 0.87–0.93). Finally, the best AdaBoost model has an F1-score of 0.92, while the group accuracy for the AdaBoost algorithm was 0.77. The use of grid search to tune the hyperparameters of the algorithms led to several optimized models per individual. To demonstrate the difference this optimization operation can have, we present an example of two individual models with different hyperparameter configurations in Table 4. Table 5 gives an overview of the number of occurrences of a value for the Random Forest hyperparameters.
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