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Many models and algorithms allow predicting driver lane-changing intents in highway. Generally speaking, the challenge consists of inferring lane-changing maneuvers from speed difference and spacing with the surrounding vehicles on current and intended lanes. In this contribution, we empirically compare two approaches: the MOBIL model and the naive Bayes algorithm. The model MOBIL is a well-established rule-based approach, while naive Bayes is a data-based classifier by machine learning. The analysis is done using naturalistic trajectories of two-lane German highways (HighD project). We identify characteristic relationships between the spacing and speed difference variables and the intent to keep lane, overtake, or fold-down. It turns out that the mechanisms initiating fold-down and overtaking are different, requiring analysing the maneuvers separately. The fold-down maneuver is a more complex process involving more surrounding vehicles in interaction. False-positive and true-negative prediction errors of lane-changing and lane-keeping intents are computed using cross-validation. The quality of prediction with the rule-based model is satisfying for overtaking and limited for the fold-down maneuver. On the other hand, the data-based algorithm, devoid of modeling bias, can improve prediction for both lane-changing maneuvers. We quantify the prediction improvement using ROC curves and demonstrate statistical significance by taking into account the number of parameters.