Leveraging machine learning (ML) for the detection of network problems dates back to handling call-dropping issues in telephony. However, troubleshooting cellular networks is still a manual task, assigned to experts who monitor the network around the clock. We present here TTrees (from Troubleshooting Trees), a practical and interpretable ML software tool that implements a methodology we have designed to automate the identification of the causes of performance anomalies in a cellular network. This methodology is unsupervised and combines multiple ML algorithms (e.g., decision trees and clustering). TTrees requires small volumes of data and is quick at training. Our experiments using real data from operational commercial mobile networks show that TTrees can automatically identify and accurately classify network anomalies - e.g., cases for which a network low performance is not apparently justified by op-erational conditions - training with just a few hundreds of data samples, hence enabling precise troubleshooting actions.