Carrier aggregation is a technology in wireless communications which allows a user to use multiple cells simultaneously for communication. In order to select cells, it is crucial to estimate their potential throughput for a given user. As a part of this estimate, we investigate how many MIMO layers a given channel can expect to use in the future, and whether machine learning can be used to predict the number of layers. Simulated user traces are used to generate training data, and special attention is directed at the construction of features based on user history. Random forests and multi-layer perceptrons are trained on the generated data, and we show that the random forests achieve better performance than baseline models, while the MLP models fail to learn and do not reach the expected performance. The importance of the used features is analysed, and we find that the history-based features are especially useful for predicting future channel ranks and thus are promising for use in a cell set selection system for carrier aggregation.