Naive Bayes predicts
From Algolit
by Algolit
Naive Bayes is a classifier that is used in many machine learning models for language comprehension. The Naive Bayes theorem was invented in the 18th century by Thomas Bayes and Pierre-Simon Laplace. With the implementation of digital technologies, it appears as an autonomous algorithmic agent, the classifier of the most simple and most used prediction models that shape our data. It is widely used in managing our mailboxes, in separating spam from non spam; but also in the analysis of how new products are received on social media and in newsfeeds. As such, it influences product design and stock market decisions.
By applying animation and experimental literary techniques this work, trained on documents of the Mundaneum, reveals the authentic voice of the algorithmic model. It provides insight into how it reads data, turns words into numbers, makes calculations that define patterns and is able to endlessly process new data and predict whether a sentence is positive or negative.
Concept, code, animation: Sarah Garcin