H2O.ai GBM Blogs
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A Look at the UniformRobust Method for Histogram Type
July 25, 2023 GBM H2O-3Tree-based algorithms, especially Gradient Boosting Machines (GBM’s), are one of the most popular algorithms used. They often out-perform linear models and neural networks for tabular data since they used a boosted approach where each tree built works to fix the error of the previous tree. As the model trains, it is continuously self-correcting. H2O-3’s GBM is […]
The Benefits of Budget Allocation with AI-driven Marketing Mix Models
September 17, 2020 AutoML Business Customer GBM GLM Machine Learning Use CasesExcerpt of the white paper: “The Latest in AI Technologies Reinvent Media and Marketing Analytics @ Allergan” Authors: Akhil Sood, Associate Director @ Marketing Sciences, Allergan Dr. Michael Proksch, Senior Director @ H2o.ai Vijay Raghavan, Associate Vice President @ Marketing Sciences, Allergan Introduction The call for accountability in marketing has been growing over recent years […]
From GLM to GBM – Part 2
July 9, 2020 Data Science Explainable AI GBM GLM Machine Learning Interpretability Responsible AI ShapleyHow an Economics Nobel Prize could revolutionize insurance and lending Part 2: The Business Value of a Better Model Introduction In Part 1, we proposed better revenue and managing regulatory requirements with machine learning (ML). We made the first part of the argument by showing how gradient boosting machines (GBM), a type of ML, can […]
From GLM to GBM – Part 1
June 9, 2020 Data Science Explainable AI GBM GLM Machine Learning Interpretability Responsible AI ShapleyHow an Economics Nobel Prize could revolutionize insurance and lending Part 1: A New Solution to an Old Problem Introduction Insurance and credit lending are highly regulated industries that have relied heavily on mathematical modeling for decades. In order to provide explainable results for their models, data scientists and statisticians in both industries relied heavily […]
H2O.ai Releases H2O4GPU, the Fastest Collection of GPU Algorithms on the Market, to Expedite Machine Learning in Python
September 26, 2017 GBM GLM GPU k-MeansH2O4GPU is an open-source collection of GPU solvers created by H2O.ai. It builds on the easy-to-use scikit-learn Python API and its well-tested CPU-based algorithms. It can be used as a drop-in replacement for scikit-learn with support for GPUs on selected (and ever-growing) algorithms. H2O4GPU inherits all the existing scikit-learn algorithms and falls back to CPU […]
H2O GBM Tuning Tutorial for R
June 16, 2016 GBM R Technical TutorialsIn this tutorial, we show how to build a well-tuned H2O GBM model for a supervised classification task. We specifically don’t focus on feature engineering and use a small dataset to allow you to reproduce these results in a few minutes on a laptop. This script can be directly transferred to datasets that are hundreds […]
