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H2O Release 3.46


By Wendy Wong | minute read | April 15, 2024

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We are excited to announce the release of H2O-3! Some of the highlights of this major release are that we added custom metric support for XGBoost, allowed grid search models to be sorted with custom metrics, and we enabled H2O MOJO and POJO to work with MLFlow. Several improvements were also made to the Uplift model (like MLI support). Another exciting update is that we now allow GLM models that were previously built to be used to calculate full loglikelihood and AIC. We also focused on fixing security vulnerabilities. Please read on for more details!

Security patch updates

H2O should always be deployed behind firewalls and in protected clusters. Many of the reported vulnerabilities assume that H2O isn’t deployed under any protection. Regardless, there are several vulnerabilities we did fix during this release:

  • CVE-2023-6016: We introduced a Java Property that disables POJO import (defaults to `disabled`) to avoid remote code execution (courtesy of Marek Novotný).

  • CVE-2023-35116: We upgraded the jackson-databind version to address potential vulnerabilities (thanks to Marek Novotný).

  • SYNK-JAVA-CIMNUMBUSDS-6247633: We upgraded the nimbus-jose-jwt version to enhance security and to mitigate potential risks (kudos to Adam Valenta).
  • CVE-2023-6038 and CVE-2023-6569: We introduced a new configuration option for filtering file system access during reading and writing in response to security concerns (credit to Bartosz Krasinski).

XGBoost support for customized metrics (Adam Valenta)

XGBoost now supports the `custom_metric_func` parameter which lets you specify any desired metric. The `custom_metric_func` parameter is well known from other algorithms like GBM and Deep Learning. To see it in action, please look to our documentation.

Loglikelihood and AIC calculation support for GLM MOJOs (Yuliia Syzon)

GLM MOJOs now support the calculation of loglikelihood and AIC given a dataset with the response column when the MOJO is loaded with the H2O Generic model. To enable this feature, you have to build the GLM model with `calc_like=True`.

MLFlow support (Marek Novotný)

We added the libraries from MLFlow (and necessary code) to enable you to use H2O-3 MOJO and POJO with the MLFlow frameworks.

Uplift DRF improvements (Veronika Maurerova)

In this release, we added many improvements for the Uplift DRF algorithm to provide you with more opportunities for tuning and interpreting your models. We added grid search, early stopping, partial dependence plots, and variable importance.

Python documentation improvements (Shaun Yogeshwaran)

We improved the Python documentation for the GAMModel Selection, and ANOVA GLM algorithms by expanding the number of available examples. More will follow in the coming releases!


Wendy Wong, Adam Valenta, Marek Novotný, Tomáš Frýda, Veronika Maurerova, Bartosz Krasinski, Yuliia Syzon, Sebastien Poirier, Hannah Tillman, Shaun Yogeshwaran


Wendy Wong

Wendy is a hacker at devising solutions to making systems smarter. Prior to working at, she was building intelligent applications on mobile devices to recognize user activities from sensor data and predict user app usage from user logs at Lab126. At Intel Labs and Aperto Networks, she was a system engineer/architect designing wireless communication systems for WiFi and cellular networks. Wendy obtained her bachelor in electrical engineering from Purdue University and a master and Ph.D. in Electrical Engineering from Cornell. She loves machine learning, swarm intelligence, mathematics and wireless communication systems. She enjoys being involved with all phases of the product development cycle; requirements analysis (what are we building and how well does it need to perform), architecture/algorithm design, performance analysis and simulation, prototyping/implementation, integration, test, and verification. Her diverse interests and skills are reflected in her patents. In her spare time, Wendy loves learning new things, being active, reading, and scuba diving. She loves the ocean and wishes she could be an amphibian someday.


Adam Valenta

Hello, my name is Adam Valenta. I am a Software Engineer at

Recently, I have obtained a master's degree in Data Science from the Czech Technical University. I have started to cooperate with in my last year at university to create my master theses aimed at Anomaly detection using Extended Isolation Forest under the supervision of Veronika Maurerová from Our successful cooperation leads me to the company. I also have a bachelor's degree in Software Engineering, and I would love to connect those two fields at the H2O by creating brilliant software for data scientists.

Before, besides my studies, I worked as a half-time Java Developer for a mid-size company where we supplied IT services for various European businesses.

In my spare time, I like to do something complementary to working at the office. I am always up for all kinds of sports. Currently, it is jogging. I want to attend Army Run in the Czech Republic next year.

I am also interested in cooking, homebrewing, motorbikes, traveling, and taking care of our summer house with my family. Finally, if I am too lazy, I am a big fan of good movies, TV shows, or computer games.