H2Oai - Neural Market Trends

Time Series for H2O with Modeltime

Modeltime ecosystem was created to help with higher frequent time series, at scale, that's automated.

Install H2O's Wave on AWS Lightsail or EC2

That's a lot of moving parts but it can be automated with a nice script that you can install upon your instances boot up.

AI Machine Learning

Isolation Forests in H2O.ai

While there are other methods of outlier detection like LOF (local outlier factor), it appears that Isolation Forests tend to be better than One Class SVM's in finding outliers.

AI Machine Learning

MLI with RSparkling

H2O-3 allows you to use Tree Shap for XGBoost and GBM algorithms. These, of course, can be used with R and Sparkling Water, so now you can generate 'reason codes' for billions of rows of data right on your cluster.

AI Machine Learning

Interpreting Machine Learning Models

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.

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Making AI Happen Without Getting Fired

The gist of the presentation is to really look at all the business connections to doing data science.

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Feature Engineering in Driverless AI

Dmitry Larko, Kaggle Grandmaster, and Senior Data Scientist at H2O.ai goes into depth on how to apply feature engineering in general and in Driverless AI. This video is over a year old and the version of Driverless AI shown is in beta form. The current version is much more

AI Machine Learning

What's new in Driverless AI?

Arno, H2O’s CTO, gave a great 1+ hour overview in what’s new with Driverless AI version 1.4.1. If you check back in a few weeks/months, it’ll be even better. In all honesty, I have never seen a companyinnovate this fast. Below

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Open Source

Is open source a bad business strategy? It can be. Is this the case here? No.

AI Machine Learning

Machine Learning Interpretability in R

In this video the presenter goes over a new R package called ‘iML.’ This package has a lot of power when explaining global and local feature importance. These explanations are critical, especially in the health field and if your under GDPR regulations. Now, with the combination of Shapley,

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Matrix Factorization for Missing Values

...I was intrigued by how this imputation method is available in H2O.ai's open-source offering. It's called 'Generalized Low Ranked Models'...

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MLI Using LIME Framework

I found this talk to be fascinating. I’ve been a big fan of LIME but never really understood the details of how it works under the hood. I understood that it works on an observation by observation basis but I never knew that it permutates data, tests against

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Exploring H2O.ai

A few years ago RapidMiner incorporated a fantastic open source library from H2O.ai. That gave the platform Deep Learning, GLM, and a GBT algos, something they were lacking for a long time. If you were to look at my usage statistics, I’d bet you’d see

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Guide to Getting Started in Data Science

This is the forward to an updated ultimate guide on getting started in data science. I wanted to write a set of ‘getting started’ posts to share with readers on how I became a data scientist at RapidMiner. How I went from a Civil Engineer with an MBA

AI Machine Learning

Introduction to Deep Learning

This is a great introduction to Deep Learning. I know I learned a few things from Phillip. Some key concepts RapidMiner now can do GPU deep learning. Supports NVIDIA. Easy: Using already loaded Neural Net operators. Harder: Using H20.ai Deep Learning operator. Hardest: Using Keras with RapidMiner. Keras requires

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Latest Writings Elsewhere

October 2016 Just a quick list of the content I’ve created some place other than this blog. This current list is 100% RapidMiner related but I’d like to branch out into guest posting. If any of my readers would like a guest post on their blog