Particle physics searches that rely on a specific signal model have so far failed to find evidence for physics beyond the Standard Model. Model-agnostic methods provide an important alternative approach, as they can analyze large amounts of data for a wide range of potential anomalies. Many state-of-the-art anomaly detection algorithms are based on a weakly supervised classification task, where the data samples are distinguished from samples of a background template. A key challenge for such algorithms is their performance degradation in the presence of uninformative features, which introduces model dependence by requiring feature selection. In this work, we propose the use of tree-based algorithms in weakly supervised anomaly detection with tabular data, as they are not only significantly faster to train and evaluate than deep learning--based methods, but are also robust to uninformative features and achieve better performance.