R Tutorial: Welcome to the Toolbox

Want to learn more? Take the full course at https://learn.datacamp.com/courses/machine-learning-with-caret-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 the machine learning toolbox course. I'm Max Kuhn, a statistician and author of the caret package, which I've been working on for over a decade. Today, caret is one of the most widely used packages in R for supervised learning, also known as predictive modeling. Supervised learning is machine learning when you have a "target variable" or something specific you want to predict. A classic example of supervised learning is predicting which species an iris is based on its physical measurements. Another example would be predicting which customers in your business will "churn" or cancel their service. In both of these cases, we have something specific we want to predict on new data: species and churn. There are two main kinds of predictive models: classification and regression. Classification models predict qualitative variables, for example, the species of a flower or "will a customer churn or not". Regression models predict quantitative variables, for example, the price of a diamond. Once we have a model, we can use a metric to evaluate how well the model works. A metric is quantifiable and it gives us an objective measure of how well the model predicts on new data. For regression problems, we will focus on root mean squared error or RMSE as our metric of choice. This is the error that linear regression models typically seek to minimize, for example, in the lm function in R. It's a good, general purpose error metric and the most common one for regression models. Unfortunately, it's common practice to calculate root mean squared error on the same data that we used to fit the model. This typically leads to over-optimistic estimates of model performance. This is also known as overfitting. A better approach is to use out-of-samp