Hello. In this lesson, you will learn how to train and evaluate regression models. Regression is where
models predict a number. In machine learning, the goal of regression is to create
a model that can predict a numeric quantifiable
value such as 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
and 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 very
useful for making predictions. For example, if a company
that rents bicycles wants to predict the expected number of rentals on a given
day in the future, a regression model can
predict this number. A model could be created
using existing data such as the number of bicycles
that were rented on days, or the season, day of the week, and so on, were also recorded. In this lesson, you
will learn when to use regression models and learn how to train and evaluate regression models using the
Scikit-Learn framework. To get the most out
of this lesson, you will need some knowledge of basic Mathematics and some experience
programming in Python.