Machine Learning

Ensemble Learning Methods for Deep Learning Neural Networks

Last Updated on August 6, 2019

How to Improve Performance By Combining Predictions From Multiple Models.

Deep learning neural networks are nonlinear methods.

They offer increased flexibility and can scale in proportion to the amount of training data available. A downside of this flexibility is that they learn via a stochastic training algorithm which means that they are sensitive to the specifics of the training data and may find a different set of weights each time they are trained, which in turn produce different predictions.

Generally, this is referred to as neural networks having a high variance and it can be frustrating when trying to develop a final model to use for making predictions.

A successful approach to reducing the variance of neural network models is to train multiple models instead of a single model and to combine the predictions from these models. This is called ensemble learning and not only reduces the variance of predictions but also can result in predictions that are better than any single model.

In this post, you will discover methods for deep learning neural networks to reduce variance and improve prediction performance.

After reading this post, you will know: