How to Develop Multi-Output Regression Models with Python

Last Updated on September 15, 2020

Multioutput regression are regression problems that involve predicting two or more numerical values given an input example.

An example might be to predict a coordinate given an input, e.g. predicting x and y values. Another example would be multi-step time series forecasting that involves predicting multiple future time series of a given variable.

Many machine learning algorithms are designed for predicting a single numeric value, referred to simply as regression. Some algorithms do support multioutput regression inherently, such as linear regression and decision trees. There are also special workaround models that can be used to wrap and use those algorithms that do not natively support predicting multiple outputs.

In this tutorial, you will discover how to develop machine learning models for multioutput regression.

After completing this tutorial, you will know:

• The problem of multioutput regression in machine learning.
• How to develop machine learning models that inherently support multiple-output regression.
• How to develop wrapper models that allow algorithms that do not inherently support multiple outputs to be used for multiple-output regression.

Let’s get started.

• Updated Aug/2020: Elaborated examples of wrapper models.

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