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3.3.2. Selection of Best Feature Set {#sec3dot3dot2-sensors-16-00339} To build the model, we first need to choose the best set of features, as the features influence the precision and recall of the model. We conducted a grid search on the train set. Each time, we added a feature to the model, and then calculated the precision and recall. From the precision and recall, we calculated the F1-score as a combined metric. We identified the best set of features using the F1-score. In this experiment, we set the search space as the number of features from 1 to 25. Note that we did not use any pre-defined parameters. We found that the optimal model was produced when we used 11 features. The best feature set was composed of features in *Car Model* (having a value of 1.0) and features in *Car Color* (having a value of 0.5). We selected the best model that achieved an F1-score of 0.73.

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