Multi-output Regression

We know in simple regression we predict a single output variable but where the goal is to predict multiple target variables instead of just one we use Multi-output regression. Multi-output regression is mostly used for addressing complex real-world problems. This approach is particularly useful when variables are interrelated or when it is more efficient to make joint predictions as the prediction of one variable can affect the prediction of other variables, we’ll understand this thing in the subtopic of chained regression.

It can be represented as follows:

where:

• y1, y2, …, ym are the output variables
• x1, x2, …, xn are the input variables
• f is the regression function

In machine learning we often encounter regression, these problems involve predicting a continuous target variable, such as house prices, or temperature. However, in many real-world scenarios, we need to predict not only single but many variables together, this is where we use multi-output regression.

In this article, we will understand the topic of multi-output regression and how to implement it using Scikit-learn in Python.