Beginning the ML process in Alteryx

ML is the process of building mathematical models to extract relationships or predict outcomes from a dataset. These models will take your input data and train the model to isolate the insight you are searching for. There are two broad categories of ML: unsupervised learning, which focuses on finding the relationships between values in your dataset, and supervised learning, which takes a target field and attempts to find the connections that can predict that target value.

This chapter will focus on how you can implement a supervised learning model in Alteryx. This focus is because all the methods for building an ML model in Alteryx focus on building supervised models. However, you can also produce unsupervised models in Alteryx using R-based predictive tools or by creating custom Python or R scripts according to the same methods we described for the supervised models. The only difference is the model algorithm you choose to deploy.

There are...