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
• Using cuML on CPU, GPU, or both