ROC curves and Area Under the Curve explained (video)

While competing in a Kaggle competition this summer, I came across a simple visualization (created by a fellow competitor) that helped me to gain a better intuitive understanding of ROC curves and Area Under the Curve (AUC). I created a video explaining this visualization to serve as a learning aid for my Data Science students, and decided to share it publicly to help others understand this complex topic. An ROC curve is the most commonly used way to visualize the performance of a binary classifier, and AUC is (arguably) the best way to summarize its performance in a single number. As such, gaining a deep understanding of ROC curves and AUC is beneficial for data scientists, machine learning practitioners, and medical researchers (among others). The 14-minute video is embedded below, followed by the complete transcript (including graphics).