# Area Under the Curve (AUC) in Plain English

AUC can be intuitively understood as: “the probability that the classifier will assign a higher score to a randomly chosen positive example than to a randomly chosen negative example.” – Wikipedia

Yeah ok nice but what does that really mean? Actually the previous intuition is a bit tricky to understand. So let’s try to understand it.

Suppose we have a binary classification problem scenario as the following: we have a dataset $X$ with instances that have either $0$ or $1$ as labels. You divide the dataset into two parts: 1- training set, 2-test test. Next you train a classifier with the training set.

# Collective Intelligence

In his book, Wisdom of the Crowds, James Surowiecki, business columnist for The New Yorker, asserts that “under the right circumstances, groups are remarkably intelligent, and are often smarter than the smartest people in them.” Surowiecki says that if the process is sound, the more people you involve in solving a problem, the better the result will be. A crowd’s collective intelligence will produce better results than those of a small group of experts if four basic conditions are met. These four basic conditions are that “wise crowds” are effective when they’re composed of individuals who have diverse opinions; when the individuals aren’t afraid to express their opinions; when there’s diversity in the crowd; and when there’s a way to aggregate all the information and use it in the decision-making process.

# Information Credibility on Twitter

With rumors and fake news being spread all over Twitter I was intrigued to learn about information credibility assessment on Twitter. Of course if you are a Twitter user and looking to increase your Followers count, then it’s intuitive to know that the more credible you look, the more followers you get. That especially applies when Continue reading