Isolation Forests in H2O.ai

Posted on 2019-10-11 in Data Science • Tagged with H2O.ai • 2 min read

A new feature has been added to H2O-3 open source, isolation forests. I've always been a fan of understanding outliers and love using One Class SVM's as a method, but the isolation forests appear to be better in finding outliers, in most cases.

From the H2O.ai blog:

There are …


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Introduction to Driverless AI from H2O.ai

Posted on 2019-07-07 in Data Science • Tagged with H2O.ai, Machine Learning • 1 min read

I finally posted a new video on my YouTube channel after a year of no activity. It felt good and is part of my 'content refresh' project I'm working on. In this video I do an introduction to Driverless AI and its EDA capabilities. The forthcoming videos will go into …


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Model Governance & Explainable AI

Posted on 2019-07-05 in Data Science • Tagged with H2O.ai, AI, Model Explainability • 1 min read

Patrick Hall of H2O.ai leads a discussion panel on model governance and explainable AI. It's close to two hours of a great discussion of a very complex but important topic.


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Interpretable Machine Learning with RSparkling

Posted on 2019-06-01 in Data Science • Tagged with H2O.ai, LIME, rSparkling • 2 min read

Last evening my colleague Navdeep Gill (@Navdeep_Gill_) posted a link to his latest talk titled "Interpretable Machine Learning with RSparkling." Navdeep is part of our MLI team and has a wealth of experience to share about explaining black boxes with modern techniques like Shapley values and LIME.

Machine Learning Interpretability …


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Interpreting Machine Learning Models

Posted on 2019-05-09 in Data Science • Tagged with H2O.ai, LIME, Shapley Values, MLI • 1 min read

Shapley Values, MLI

I found this short 8 minute video from H2O World about Machine Learning Interpretability (MLI). It's given by Patrick Hall, the lead for building these capabilities in Driverless AI.

My notes from the video are below:

  • ML as an opaque black box is no longer the case
  • Cracking the black …

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