June 7th, 2013

Chocolate Cake

RSS icon RSS Category: Uncategorized [EN]
Fallback Featured Image

Chocolate Cake (Wednesday, June 5, 2013) 

You know how sometimes you have one bite of really good chocolate cake, or a really amazing peach and totally assume that you could eat another 30lbs of whatever without regard for good manners or physical limitations?  Yeah. Decreasing marginal returns dictate that it almost always turns out that the last bite isn’t as good as the first one – having a little and having a lot are different.

Similarly, ingesting 1000 bytes of data and 1 byte are pretty different, and when you’re used to little bytes and start fooling around with the big ones the differences might not be immediately obvious or intuitive (maybe they are, and if that’s the case – awesome! Now go eat your cake).

When I’m trying to make sense of a problem I like to start with a small example and work through it to get a feel for the mechanics. With Big Data this gets a little weird, since we’re almost always mining, so we don’t always know well what to look for or expect, and because we need some intuition for how to get from the small to the big.  To help that, I am trying to build some intuitive explanations.  You can look at them topically under the posts beginning with header “Big vs. Little…”

Sometimes it is the case that using H2O to look at small data sets really makes no sense for whatever reason. In those cases we’ll talk about why, and I’ll use R for comparison. I’ll also provide you with relevant output for each (so that you can see how to get from one to the other). If you’re not familiar with R go here.  Additionally, it’s worth mentioning that I’m tackling one set of assumptions at a time, so in general I’ll work as though we are going through some ad-hoc analysis instead of post-hoc analysis.  There are some super cool differences between mucking vs. mining, but I want to talk about those separately.

Leave a Reply

+
Three Keys to Ethical Artificial Intelligence in Your Organization

There’s certainly been no shortage of examples of AI gone bad over the past few

September 23, 2022 - by H2O.ai Team
+
Using GraphQL, HTTPX, and asyncio in H2O Wave

Today, I would like to cover the most basic use case for H2O Wave, which is

September 21, 2022 - by Martin Turoci
+
머신러닝 자동화 솔루션 H2O Driveless AI를 이용한 뇌에서의 성차 예측

Predicting Gender Differences in the Brain Using Machine Learning Automation Solution H2O Driverless AI 아동기 뇌인지

August 29, 2022 - by H2O.ai Team
+
Make with H2O.ai Recap: Validation Scheme Best Practices

Data Scientist and Kaggle Grandmaster, Dmitry Gordeev, presented at the Make with H2O.ai session on

August 23, 2022 - by Blair Averett
+
Integrating VSCode editor into H2O Wave

Let’s have a look at how to provide our users with a truly amazing experience

August 18, 2022 - by Martin Turoci
+
5 Tips for Improving Your Wave Apps

Let’s quickly uncover a few simple tips that are quick to implement and have a

August 9, 2022 - by Martin Turoci

Start Your Free Trial