Experiments with Machine Learning & Lex: Teaching a Machine to Identify Color

Amazon has (somewhat) recently added some new services under the Artificial Intelligence offerings, one of them being a Machine Learning service. I wanted to play around with their predictive analysis service so I decided to make a really simple proof of concept.

Predictive analysis, in a nutshell, is basically looking through a large dataset of various input values that each contain an outcome. That outcome may be a true or false conditional (Binary Classification), a numerical value (Regression), or identifying a label (Multiclass Classification). This data is used to generate a model that makes a correlation between the input variables and the outcome, which can then be fed new input values to predict what the outcome will be. The catch, of course, is that you need to have this large set of training data to work with.

Since I didn’t have any data available, I wanted to see what I could possibly generate on my own. I decided on trying to make a model that could guess the name of a color based on the input value. The end result would look something like the following (once integrated into slack):

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