R Tutorial: Machine Learning with Tree-Based Models | Intro

Want to learn more? Take the full course at https://learn.datacamp.com/courses/machine-learning-with-tree-based-models-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Welcome to Machine Learning with Tree-Based Models in R! I am Erin LeDell and I'm a Machine Learning Scientist and co-author of several R packages including the h2o package for machine learning. I'm Gabriela de Queiroz and I'm a Data Scientist and the founder of R-Ladies, a world-wide organization for promoting diversity in the R community. Supervised learning is the subfield of machine learning in which you train a model using input data and corresponding labels. The converse is called unsupervised learning, where you learn from the input data alone. In supervised learning, each example is a pair consisting of the input data and an output value which represents a category or label in the case of classification, or a numeric value in the case of regression. A supervised learning algorithm analyzes the training data and produces an inferred function, or a "model", which can be used for mapping new examples to predicted labels or values. As an analogy, you can compare supervised learning to a student learning a subject by studying a set of questions and their corresponding answers. After mastering the mapping between questions and answers, the student can then provide answers to new (never-before-seen) questions on the same topic. In this course we'll talk about decision tree-based models, including tree-based ensemble models such as Random Forests and Gradient Boosting Machines (or GBMs). Tree-based models stand out from other types of machine learning models due to their unique combination of model interpretability, ease-of-use, and, when used in ensembles, excellent accuracy. Tree-based methods are simple and useful for model interpretation. They are used to make decisions, explore the data and make predictions.