This document provides an agenda and overview for a meetup about using the machine learning platform H2O. H2O can handle large datasets with billions of rows and hundreds of gigabytes of data. It performs computations very quickly, near Fortran speeds. The meetup will demonstrate using H2O with the MNIST handwritten digit recognition dataset and a bodily injury claims dataset from Allstate. For the MNIST data, random forest classification models will be built and the class label counts inspected. For the Allstate data, generalized linear models will be demonstrated for supervised prediction tasks.

1. 4/23/13
Hack Data With
Math
Meetup 8/29/2013

2. Introduction
What H2
O is:
● Machine learning platform
● Distributed
● In-memory
● Open Source
What H2
O can do:
● Scales your analysis:
● Handles large datasets: Billions of rows, 100s of GBs
● Performs computations at very quickly (near Fortran speeds)

3. Agenda
Use H2O with these data:
● MNIST Data
● Kaggle Allstate Data

4. MNIST DATA: Recognizing Handwritten Digits

5. MNIST Data
➢ Each observation has ~800 features, one
feature for each pixel in the image
➢ Each feature observation ranges from 0 to
255 where 0 is blank and 255 is totally black
➢ There are ~60K observations total
0 1 ……... 783 784
5 0 ……... 231 255
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1 120 ……... 4 0
Class Labels Pixel Values

6. Random Forest
Random Forest For Classification
➢ Build a committee of decorrelated decision
trees, call it a forest
➢ Give data to the committee for prediction
➢ Majority vote on a row of data to classify

7. Random Forest
Pros
➢ Decision trees model complex
interactions
➢ Committee of trees reduces
classification error
➢ Easy to train and tune on small
data

8. MNIST Data Class Label Counts
Inspect data by piping together command line tools into lengthy
statements...
Or use H2O! Demo time!
Direct your browser to 192.168.1.161:xxxxx
xxxxx is your provided port number

9. Bodily Injury Claims: Allstate Data

10. Generalized Linear Modeling (GLM)
Supervised Learning For Prediction:
>Train on data with known labels
>Validate on out of sample data with known labels
>Test on new data
● Enough training data for the model to adequately capture
complex interactions between variables
● Use shrinkage methods to improve predictive power
● A model can be mostly judged by its “ability to predict”
○ Model is no good when predictive power falls below
some threshold
Examples:
○ Regression: Linear, Logistic, Poisson, Tweedie

11. Allstate Kaggle Data
Demo 2

12. Thanks!