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cuml 23.04.00 documentation

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  • Introduction
  • API Reference
  • User Guide
  • Blogs and other references

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  • Training and Evaluating Machine Learning Models
  • Pickling Models for Persistence
  • Using cuML on CPU, GPU, or both
  • User Guide

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

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