Existing proposals for positioning in NB-IoT networks based on range estimation are characterized by either low accuracy or lack of compliance with 3GPP standards. While range-free approaches taking advantage of Machine Learning (ML) have been recently proposed as a potential way forward, their evaluation has been carried out only in simulated environments, with the exception of Weighted k Nearest Neighbours (WkNN), recently tested on experimental data. This work inves-tigates four ML strategies for range-free positioning in NB-IoT networks, based on WkNN and its combination with preprocessing and classification algorithms as well as on Artificial Neural Networks (ANNs). The strategies are evaluated on experimental data and are compared based on a set of Key Performance Indicators (KPIs) measuring both positioning performance and computational complexity. Results show that range-free positioning using ML is a viable solution in commercial NB-IoT networks, and that WkNN and ANNs are at the two extremes in terms of a performance/complexity trade-off; intermediate trade-offs can be achieved by combining WkNN with preprocessing techniques and classification models.