A Repost of a CitizenNet Blog Post about Random Forests, Neural Networks, and Ensembles
Confidence is an important but problematic concept in machine learning. It is an inconvenient truth that our models are only as good as the results of our investigations. We like to think of Science as: we gather data, we make a theory about the data, and the theory is an explanation of what is going on.
CitizenNet’s Audience Map is a tool that organizes everything about your current and future customers, leveraging the CitizenNet predictive database and Facebook advertising in unison.
Another great CitizenNet post on Facebook and media returns—this time, for box office.
The referenced paper is second in a series examining the relationship between online social behavior and offline purchase behavior.
We live in a world awash in data. From our digital pedometers to the words we write, the amount of data generated in two days is greater than all data collected from the dawn of time to 2003.
Take two messages: “I am going to no doubt — love them!” vs “I am going to no doubt” If you were searching for messages about the band No Doubt, which one would you include?
Behind the scenes, a robust prediction system builds the targets for the project. This prediction system is trained on past behavior, which it then uses to predict how a future (unknown) project may be best targeted. One of the components of the prediction system is a classifier, which is a currently an ensemble of both Neural Networks and Random Forest classifiers. This blog post aims to illustrate the basics of these methods.