Why Use Ensemble Learning? In this tutorial, you will discover the benefits of using ensemble methods for machine learning.
Ensembles are predictive models that combine predictions from two or more other models.
Ensemble learning methods are popular and the go-to technique when the best performance on a predictive modeling project is the most important outcome.
Nevertheless, they are not always the most appropriate technique to use and beginners the field of applied machine learning have the expectation that ensembles or a specific ensemble method are always the best method to use.
Ensembles offer two specific benefits on a predictive modeling project, and it is important to know what these benefits are and how to measure them to ensure that using an ensemble is the right decision on your project.
In this tutorial, you will discover the benefits of using ensemble methods for machine learning.
After reading this tutorial, you will know:
Let’s get started.
Don’t they do the same thing? Why Deep Learning Ensembles Outperform Bayesian Neural Networks
In this post I will cover ensemble learning types, advanced ensemble learning methods — Bagging, Boosting, Stacking and Blending with code samples. At the end I will explain some pros and cons of using ensemble learning.
The word Ensemble refers to a group of objects and viewing them as a whole. The same definition applies even for Ensemble modeling in machine learning in which a group of models are considered together to make predictions.
Ensembles: the almost free Lunch in Machine Learning. Build optimal ensembles of neural networks with PyTorch and NumPy
Project walk-through on Convolution neural networks using transfer learning. From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects.