New Course Available Now: Machine Learning with Tidymodels

The ever increasing application of machine learning models in industry and academia requires tools which are easy to use and ensure a reliable model fitting process. The R package universe covers practically all statistical models on the planet including all relevant machine learning models like neural nets, support vector machines, decision trees, and random forests. However, most of these packages do not provide a consistent interface, which makes it hard to fit and compare models from different families. Even worse, it is hard to create standardized workflows for typical machine learning projects which ensure that

• no information has been leaked from the training data, leading to higher performance numbers.
• models are compared on the same re-sampling procedures.
• performance metrics are calculated correctly.

The tidymodels framework is a new package ecosystem, in which all steps of the machine learning workflow are implemented through dedicated R packages. The consistency of these packages ensures their interoperability and ease of use. Most importantly, the framework makes your machine learning workflow easier to understand and faster to implement. tidymodels should definitely be part of every R data scientist’s tool box. Additionally, it fits perfectly into the tidyverse package ecosystem and provides excellent compatibility with packages like dplyr or ggplot2.