2 39 MACHINE LEARNING ENABLED PERSONALIZED PHYSICAL ACTIVITY COACHING Table 4. Example of different tuned personalized Random Forest models. Participant Parameters Values 1119 criterion max_features n_estimators gini sqrt 50 1121 criterion max_features n_estimators entropy log2 50 Table 5. The number of different values per Random Forest hyperparameter. Hyperparameter Value Number of Occurrences criterion entropy 7 gini 37 max_features 0.1 4 0.25 5 0.5 7 0.75 15 log2 2 sqrt 2 null 9 n_estimators 10 3 100 17 50 16 500 6 The accuracy and F1-score of the various machine learning algorithms increase slightly during the day. The size of this increase differs slightly per machine learning algorithm. For instance, the F1-score of Random Forest increases with 10% during the day, starting with an F1-score of 0.89 at 7:00 AM and ending with an F1-score of 0.97 at 6:00 PM. Both Figures 4 and 5 also show the increase in accuracy and F1-score of the baseline algorithm during the day. Its accuracy starts with 0.55 and ends at 1 at the end of the workday, while the F1-score starts at 0 and ends at 1. The accuracy increases for Random Forest, Logistic Regression, and AdaBoost, whereas the accuracy of Neural Networking is best at 11:00 AM and Stochastic Gradient Descent remains the same.
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