H2O.ai H2O-3 GBM Algorithm

Gradient Boosting Machine from H2O-3 Core Library

Overview

Gradient Boosting Machine (for Regression and Classification) is a forward learning ensemble method. The guiding heuristic is that good predictive results can be obtained through increasingly refined approximations. H2O’s GBM sequentially builds regression trees on all the features of the dataset in a fully distributed way - each tree is built in parallel.

Highlights

H2O’s Gradient Boosting Algorithms follow the algorithm specified by Hastie et al (2001)

Details

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H2O.ai H2O-3 GBM Algorithm

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Amazon SageMaker algorithm

An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.

Deploy the model on Amazon SageMaker AI using the following options:

Algorithm training

Before deploying the model, train it with your data using the algorithm training process. You're billed for software and SageMaker infrastructure costs only during training. Duration depends on the algorithm, instance type, and training data size. When training completes, the model artifacts save to your Amazon S3 bucket. These artifacts load into the model when you deploy for real-time inference or batch processing. For more information, see Use an Algorithm to Run a Training Job .

Real-time inference

Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference .

Batch transform

Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI .

Version release notes

initial release of H2O.ai H2O-3 GBM algorithm

Additional details

Inputs

Summary: See documentation for list of all available parameters that can be passed to the algorithm. NOTES: only parameter required is "training" hyperparameter. Please make sure to define "distribution" if the expected target is categorical. Or be sure to define "categorical_columns" with the specific categorical columns in the dataset.

Input MIME type: csv, text/csv, s3

Real-time inference sample input data

See Input Summary

Batch transform sample input data

See Input Summary

Resources

Vendor resources

H2O.ai Documentation for Gradient Boosting Machine Algorithm

H2O.ai Complete Documentation

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