2 35 MACHINE LEARNING ENABLED PERSONALIZED PHYSICAL ACTIVITY COACHING Heroku is used to host a demo version of the Web application. This Web application is available at http://personalized-coaching.compsy.nl. The Web application is available as open-source software on Github https://github.com/compsy/personalized-coaching-app). 4. RESULTS After optimizing our machine learning models by applying grid search in combination with cross-validation, we assessed the models using the test set. The results are presented here. 4.1. Accuracy and F1-Score on Group Level Table 2 presents the F1-score and accuracy of the eight different algorithms at the group level. The top three group algorithms based on the mean accuracy and F1-score are: AdaBoost, Neural Networking, and Support Vector Classifier. Table 2. Algorithms and their scores for the whole dataset. Algorithm Name Mean Accuracy (standard deviation) Mean F1 (standard deviation) Rank AdaBoost (ADA) 0.776623 (0.002080) 0.854157 (0.001626) 1 Neural Networking (NN) 0.777774 (0.001545) 0.852797 (0.002938) 2 Support Vector Classifier (SVC) 0.770728 (0.002505) 0.856341 (0.002405) 3 Stochastic Gradient Descent (SGD) 0.767623 (0.005490) 0.853575 (0.004574) 4 KNeighborsClassifier (KNN) 0.749171 (0.005683) 0.829826 (0.005544) 5 Logistic Regression (LR) 0.742125 (0.009821) 0.825725 (0.008487) 6 Random Forest (RF) 0.737451 (0.003210) 0.819065 (0.003840) 7 Decision Tree (DT) 0.720535 (0.004787) 0.804220 (0.003006) 8 We visualized the accuracy and F1-score per algorithm using boxplots in Figures 1 and 2. The box represents the second and third quartile groups and the red line indicates the median. The upper whisker visualizes the fourth quartile group and the lower whisker visualizes the first quartile group. Finally, the plus sign indicates outliers on either side of both whiskers.
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