Introduction
Regression is a technique that uses models to predict a number.
In machine learning, the goal of regression is to create a model that can predict a numeric, quantifiable value. This value can be a price, amount, size, or other scalar number.
Regression is a statistical technique of fundamental importance to science because of its ease of interpretation, robustness, and speed in calculation. Regression models provide an excellent foundation to understanding how more complex machine learning techniques work.
In real-world situations, particularly when little data is available, regression models are useful for making predictions. For example, if a company that rents bicycles wants to predict the expected number of rentals on a day in the future, a regression model can predict this number. The company can create a model by using existing data like the number of bicycles rented on days where the season and day of the week were also recorded.
Prerequisites
- Knowledge of basic mathematics
- Some experience programming in R
Learning objectives
In this module, you'll learn:
- When to use regression models.
- How to train and evaluate regression models by using the tidymodels framework.
Produced in partnership with Eric Wanjau - Microsoft Learn Student Ambassador and Researcher/Data Scientist: Leeds Institute for Data Analytics, University of Leeds