Note: this is a guest blog post by Jaafar Almusaad. How Much is My Property Worth? This is the million-dollar question – both figuratively and literally. Traditionally, qualified property valuers are tasked to answer this question. It’s a lengthy and costly process, but more critically, it’s inconsistent and largely subjective. Mind you, valuation is an […]

H2O-3 is, at its core, a platform for distributed, in-memory computing. On top of the distributed computation platform, the machine learning algorithms are implemented. At H2O.ai, we design every operation, be it data transformation, training of machine learning models or even parsing to utilize the distributed computation model. In order to work with big data […]

H2O.ai engineers continually innovate and introduce new techniques by adopting latest research, working on cutting edge use cases, and participating in and winning machine learning competitions like Kaggle. But thanks to the explosion of AI research and applications even the most advanced automated machine learning platform like H2O Driverless AI cannot come with all bells and whistles to satisfy every […]

There were two releases shortly after each other. First, on December 21st, there was a minor (fix) release 3.22.0.3. Immediately followed by a more major release (but still on 3.22 branch) codename Xu, named after mathematician Jinchao Xu, whose work is focused on deep neural networks, besides many other fields of research. Of course, the […]

Creating and plotting decision trees (like one below) for the models created in H2O will be the main objective of this post: Figure 1. Decision Tree Visualization in R Decision Trees with H2O With release 3.22.0.1 H2O-3 (a.k.a. open source H2O or simply H2O) added to its family of tree-based algorithms (which already included DRF, […]

Ask any data scientist in your workplace. Any Data Science Supervised Learning ML/AI project will go through many steps and iterations before it can be put in production. Starting with the question of “Are we solving for a regression or classification problem?” Data Collection & Curation Are there Outliers? What is the Distribution? What do […]

In September, H2O.ai released a new open source software project for GPU machine learning called H2O4GPU. The initial release (blog post here) included a Python module with a scikit-learn compatible API, which allows it to be used as a drop-in replacement for scikit-learn with support for GPUs on selected (and ever-growing) algorithms. We are proud […]

Prepared by: Erin LeDell and Navdeep Gill Stacked Ensembles H2O’s new Stacked Ensemble method is a supervised ensemble machine learning algorithm that finds the optimal combination of a collection of prediction algorithms using a process called stacking or “Super Learning.” This method currently supports regression and binary classification, and multiclass support is planned for a […]

(Just from my point of view as a package maintainer.) New users of R might not appreciate the full benefit of CRAN and new package maintainers may not appreciate the importance of keeping their packages updated and free of warnings and errors. This is something I only came to realize myself in the last few […]

This post is reposted from Rstudio’s announcement on sparklyr – Rstudio’s extension for Spark Connect to Spark from R. The sparklyr package provides a complete dplyr backend. Filter and aggregate Spark datasets then bring them into R for analysis and visualization. Use Spark’s distributed machine learning library from R. Create extensions that call the full […]