R Tutorial: Parameters vs hyperparameters

Want to learn more? Take the full course at https://learn.datacamp.com/courses/hyperparameter-tuning-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 this course on hyperparameter tuning in R. In this course you will learn: - what hyperparameters are and what makes them different from regular parameters - why hyperparameter tuning is an important step towards optimizing your machine learning models - and how you can apply hyperparameter tuning with the packages caret, mlr and h2o. My name is Shirin and I started out as a traditional biologist. I spent a lot of time in the lab. But eventually, it became clear that what I really enjoyed above all else was working with data. That's why I spent two years as a bioinformatics Postdoc at the University of Münster in Germany before I started working as a Data Scientist for codecentric. I also write a Data Science blog where I play around with different datasets, analyses, and visualization techniques. So, why do we use the strange word "hyper-parameter"? And how are hyperparameters different from model parameters? In this chapter, we will work with a dataset about breast cancer patient samples. 10 features describe the diagnosis of benign or malignant tissue masses. Here, we use them to build a classification model. Let's have a look at a simple linear model. A linear model models the relationship between variables by fitting a linear function. Here, we will pick two features at random: perimeter_worst & fractal_dimension_mean and look at their linear relationship. We could, of course, make our linear model much more complex by adding additional features and more complex interactions, but for this purpose, we will keep it simple. The summary function will give us an overview of the fitted linear model and its results, like residuals, coefficients, and statistics. The results of our fitted linear model give the model parameters.