User Guide# Training and Evaluating Machine Learning Models Shared Library Imports Random Forest Classification and Accuracy metrics UMAP and Trustworthiness metrics DBSCAN and Adjusted Random Index Linear regression and R^2 score Pickling Models for Persistence Single GPU Model Pickling Distributed Model Pickling Exporting cuML Random Forest models for inferencing on machines without GPUs cuML on GPU and CPU Installation Cross Device Training and Inference Serialization Conclusion