Bootstrap aggregation, or bagging, is a popular ensemble method that fits a decision tree on different bootstrap samples of the […]
Archive | Ensemble Learning
A Gentle Introduction to Ensemble Diversity for Machine Learning
Ensemble learning combines the predictions from machine learning models for classification and regression. We pursue using ensemble methods to achieve […]
A Gentle Introduction to Multiple-Model Machine Learning
An ensemble learning method involves combining the predictions from multiple contributing models. Nevertheless, not all techniques that make use of […]
Essence of Boosting Ensembles for Machine Learning
Boosting is a powerful and popular class of ensemble learning techniques. Historically, boosting algorithms were challenging to implement, and it […]
Ensemble Machine Learning With Python (7-Day Mini-Course)
Ensemble Learning Algorithms With Python Crash Course. Get on top of ensemble learning with Python in 7 days. Ensemble learning […]
How to Develop a Weighted Average Ensemble With Python
Weighted average ensembles assume that some models in the ensemble have more skill than others and give them more contribution […]
Strong Learners vs. Weak Learners in Ensemble Learning
It is common to describe ensemble learning techniques in terms of weak and strong learners. For example, we may desire […]
A Gentle Introduction to Mixture of Experts Ensembles
Mixture of experts is an ensemble learning technique developed in the field of neural networks. It involves decomposing predictive modeling […]
Growing and Pruning Ensembles in Python
Ensemble member selection refers to algorithms that optimize the composition of an ensemble. This may involve growing an ensemble from […]
Dynamic Ensemble Selection (DES) for Classification in Python
Dynamic ensemble selection is an ensemble learning technique that automatically selects a subset of ensemble members just-in-time when making a […]