Potential limitations of the tree-based approach compared to neural network-based out-of-distribution detection methods include limited capacity to capture complex nonlinear relationships, scalability issues with high-dimensional data, and potential overfitting on noisy or sparse data. To address these limitations, techniques such as ensemble learning with a combination of different tree-based models, regularization methods to prevent overfitting, and feature engineering to enhance the model's ability to extract relevant information from the data can be employed. Additionally, exploring hybrid models that combine the interpretability of tree-based methods with the flexibility of neural networks could offer a more robust and accurate out-of-distribution detection solution.