Python Tutorial : Extreme Gradient Boosting with XGBoost

Want to learn more? Take the full course at https://learn.datacamp.com/courses/extreme-gradient-boosting-with-xgboost at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, my name is Sergey Fogelson and I'm the instructor for Datacamp's course on Gradient Boosted Trees With XGBoost. I'm a data scientist working in the media industry and have used XGBoost extensively on a variety of machine learning problems. I've created this course with DataCamp to help others quickly understand how to use this very popular implementation of gradient boosting. Let's get started. In order to understand XGBoost, we need to have some handle on the broader topics of supervised classification, decision trees, and boosting, which we will cover throughout this chapter. To begin, let's briefly review what supervised learning is and the kinds of problems its methods can be applied to. At its core, supervised learning, which is the kind of learning problems that XGBoost can be applied to, relies on labeled data. That is, you have some understanding of the past behavior of the problem you're trying to solve or what you're trying to predict. For example, assessing whether a specific image contains a person's face, is a classification problem. Here the training data are images converted into vectors of pixel values, and the labels are either 1 when the image contains a face or 0 when the image doesn't contain a face. Given this, there are two kinds of supervised learning problems that account for the vast majority of use-cases: classification problems and regression problems. We will only talk about classification problems here and leave regression to chapter 2. Classification problems involve predicting either binary or multi-class outcomes. For example, predicting whether a person will purchase an insurance package given some quotes is a binary supervised learning problem, and predicting whether a picture contains one of se