Deep Learning Models for Multi-Output Regression
Last Updated on August 28, 2020
Multi-output regression involves predicting two or more numerical variables.
Unlike normal regression where a single value is predicted for each sample, multi-output regression requires specialized machine learning algorithms that support outputting multiple variables for each prediction.
Deep learning neural networks are an example of an algorithm that natively supports multi-output regression problems. Neural network models for multi-output regression tasks can be easily defined and evaluated using the Keras deep learning library.
In this tutorial, you will discover how to develop deep learning models for multi-output regression.
After completing this tutorial, you will know:
- Multi-output regression is a predictive modeling task that involves two or more numerical output variables.
- Neural network models can be configured for multi-output regression tasks.
- How to evaluate a neural network for multi-output regression and make a prediction for new data.
Let’s get started.
Deep Learning Models for Multi-Output Regression
Photo by Christian Collins, some rights reserved.
Tutorial Overview
This tutorial is divided into three parts; they are:
- Multi-Output Regression