This document discusses using Sparkling Water to build machine learning applications with H2O.ai. It provides an example of using Sparkling Water and H2O algorithms like Word2Vec and GBM to classify tech news articles based on short summaries. The analysis shows author information is an important variable for classification, but using only text blurbs still achieves around 70% accuracy on the second classification attempt. Potential applications of this type of text classification are suggested.

1. H2O.ai
Machine Intelligence
BUILDING MACHINE LEARNING
APPLICATIONS WITH SPARKLING WATER
AV N I WA D H WA & V I N O D I Y E N G A R

2. H2O.ai
Machine Intelligence
Sparkling Water
• Seamless integration of H2O with Spark ecosystem
• Transparent use of H2O data structures and algorithms
with Spark API
• Excels in existing Spark workflows requiring advanced
Machine Learning algorithms
Provides the following:

3. H2O.ai
Machine Intelligence
Sparkling Water Requirements
• Spark Version 1.4
• Sparkling Water 1.4.3 (download at h2o.ai/download)

4. H2O.ai
Machine Intelligence
Tech News Use Case
• The goal is to predict the tag based on the short summary of the
article

5. H2O.ai
Machine Intelligence
Tech News Use Case— Crawler
Used import.io to create a crawler which went through numerous pages of techcrunch.com
news and and acquired data regarding the title of the article, the author, a 2-3 sentence
opening from the beginning of the articles, and the tags associated with the article

6. H2O.ai
Machine Intelligence
Tech News Use Case
First manipulation of words involves eliminating words that could
occur frequently and do not add value to the classification process.
Sample Scala code:

7. H2O.ai
Machine Intelligence
Tech News Use Case
We now eliminate words that do not add value to the
classification process
• ie punctuation, stopwords, and words that do not occur
frequently
Sample Scala code:

8. H2O.ai
Machine Intelligence
Tech News Use Case — Word2Vec
A mathematical way to represent a word as a vector of numbers.
These vector ‘representations’ encode information about the
given word. In other words, the vector captures the meaning of
the word.

9. Text blurb
Word2Vec
Model
GBM
Model
Word2Vec
Categorize
the text
Train a model
“This article is
related to gadgets”
“Apple has been tinkering
with ways to make
the iPhone better at managing battery life…”
Article Blurb
Tech News Work Flow

10. H2O.ai
Machine Intelligence
Category Information
The original data set yielded about 55
categories. In order to streamline the
classification process, we chose the 14
most frequently appearing tags in our
dataset and labeled the rest into a
catch-all category titled “Other.” The
figure to the right shows the
distribution of data in each category.
Category Information
The variable importance chart to the right shows that
the author holds an overwhelming majority when it
comes to importance among variables. In other
words, the classification took place using very little
information from the text samples provided and
came mostly from authors that frequently write
under the same article tag. Let’s see how this changes
when we try to classify the articles using only the text
samples.

11. H2O.ai
Machine Intelligence
Analysis
The validation confusion matrix below is for the model that used both the authors and text blurbs to
categorize articles. We know that in this model, there was a heavy variable importance placed on
authors. In the confusion matrix below, we see how this effects the error rate of various tags. For tags
with smaller sets of data, it is common that a few authors write the majority of articles associated with
those tags. For the “Enterprise” tag for example, the data set is relatively small, and the error rate is
relatively low (40%).

12. H2O.ai
Machine Intelligence
Analysis
The validation confusion matrix below is for the model that uses text blurbs exclusively to categorize
articles. If we look at the error rate on the “Enterprise” tag, we see that the error rate is 75%,
significantly higher than the error rate we saw when authors were incorporated into the data. This
shows the strength in the variable importance of the authors.

13. H2O.ai
Machine Intelligence
Example Classification
With the Scala code below, we identify and author of an article and a
the snippet of the article provided, and try to classify what the article is
about.

14. H2O.ai
Machine Intelligence
Hit Ratios
With Authors Without Authors
Hit ratios illustrate the chances of your model correctly categorizing a text blurb on the 1st,
2nd, 3rd, etc. try. The above charts show that both the model that do and do not include
authors have approx. 70% chance of correctly predicting a text blurb on the second try.

15. H2O.ai
Machine Intelligence
Possible Use
A possible use for such classification capabilities would be for blog
posting sites. The user would enter their text into the field, and the
classification model would automatically choose tags for the post.

16. H2O.ai
Machine IntelligenceCustomers • Community •
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