Explorations in Machine Learning

An Evaluation of Algorithms and Visualization Interfaces

Summer 2015Rahul Jayaraman

Brown University Class of 2019

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)

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

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: A Training 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

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

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

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

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

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)

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

Reactivity

User changes

these data

fields

This map updates

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