The document evaluates various machine learning algorithms using h2o.ai to analyze census data for income predictions. It highlights the effectiveness of algorithms like linear regression and discusses the benefits of using R packages, specifically Shiny, for data visualization. The project emphasizes the accessibility of machine learning tools and their importance in data analysis.

1. Explorations in Machine
Learning
An Evaluation of Algorithms andVisualization Interfaces
Summer 2015
Rahul Jayaraman
Brown University
Class of 2019

2. Purpose
• Evaluate the efficiency of various machine learning algorithms using a
machine-learning-as-a-service interface (i.e. available to consumers)
• Learn the different configurations necessary for each algorithm to ensure an
accurate prediction
• Learn a scripting language (R) and use some packages to visualize the data
• Specifically, I plan on using Census data to determine the correlations
between demographics and income ( > or < $50K/yr)

3. Choice of ML Interface
• I picked h2o.ai as the base of my machine learning exploration
• It has a simple, easy-to-use user interface
• It is integrable with R (there is an R package developed for h2o.ai)
• It is readily available to download and only requires basic knowledge of
Terminal commands to set up and start
• Data used was from the UC Irvine ML database, a frequently-cited starting
point for anyone who wants to go deeper into the science of ML

5. Building ML Models
• The UCI database contains a variety of data sets about everything from
color values to animal habitats to population income
• Each folder includes two documents: ATraining set + Actual data
• First we input the training set to build various models based on the variables present
• Then, we must configure the appropriate settings in h2o.ai (i.e. iterations, etc.)
• Finally, we apply the models to the actual data
• For this project, I used population/income data from 1994 US Census polls

6. Applying Algorithms (1) - Linear Regression
• Linear regression is an ML algorithm
in which each variable is assigned a
coefficient denoting its importance
to obtaining the end result
• E.g. 2x + 5y + 3z + 4a (y is highest imp.)
• The length of the bar suggests the
strength of the correlation
• Blue suggests a positive correlation,
and orange suggests a negative
correlation
• Runtime of < 0.001 s

7. Applying Algorithms (2) – Others
• Naïve Bayes (probabilistic) – 0.03 s
• It takes each factor into account independently and then evaluates the probability of
the desired result based upon the given factors
• K-Means Clustering (ineffective in this case) – 2 s
• Clusters data into groups of data points
• Implementation is similar to Least Squares Algorithm
• Deep Learning (takes a long time, very sensitive to small changes) – 15-30 m
• The results of these three algorithms are less useful in the context of the
problem at hand

8. End Result of ML Models – Accurate?
• The methods seemed to give the desired result, and h2o.ai is an intuitive
application to work with
• This is from 1994 census data; if more data from later years is released
(somewhere online), the reliability of these models can be tested
• These models can also be tested on similar data from other domains
• Demonstrates clear importance of having a test + validation method

9. Visualization of Data
• Numerous visualization packages (graphers, etc) exist in various languages
• To visualize my data, I picked Shiny, a package for the R scripting language
• It is simple, easy-to-use, and has high commonality with HTML

10. The Nuts and Bolts of Shiny
• 2 Files
• Server.R – handles the backend
• UI.R – what the user sees
• Data and pictures in a separate folder
• Helper files in the same folder (other scripts used to download files and feed
them to the other two files)

11. Shiny Is Function-Based (like R)
• UI document starts with a function defining UI
• Two main panels in the UI – and each can have a
variety of objects in them (images, text, graphs, etc.)
• The UI file and the Server file are mutually dependent
– if a variable is named in the Server file, with the
appropriate code it can be used in the UI file without
any messy code in R
• The server has one function with an input and output
the programmer defines

12. Reactivity
This map updates

13. Conclusions
• It’s really easy to get involved with machine learning – just download a
program, input the necessary settings, and then go!
• As datasets and their analysis become more popular, it’s important to find a
tool to reliably show “what’s important” about the data or “why should I
care” – and Shiny (R) fills that niche simply because it’s easy to use
• ML is clearly “the next big thing,” and it’s always exciting to be at the
forefront of such a novel technology