It is common to describe ensemble learning techniques in terms of weak and strong learners. For example, we may desire to construct a strong learner from the predictions of many weak learners. In fact, this is the explicit goal of the boosting class of ensemble learning algorithms. Although we may describe models as weak or strong generally, the terms have a specific formal definition and are used as the basis for an important finding from the field of computational learning […]

Weighted average ensembles assume that some models in the ensemble have more skill than others and give them more contribution when making predictions. The weighted average or weighted sum ensemble is an extension over voting ensembles that assume all models are equally skillful and make the same proportional contribution to predictions made by the ensemble. Each model is assigned a fixed weight that is multiplied by the prediction made by the model and used in the sum or average prediction […]

Ensemble Learning Algorithms With Python Crash Course.Get on top of ensemble learning with Python in 7 days. Ensemble learning refers to machine learning models that combine the predictions from two or more models. Ensembles are an advanced approach to machine learning that are often used when the capability and skill of the predictions are more important than using a simple and understandable model. As such, they are often used by top and winning participants in machine learning competitions like the […]

Boosting is a powerful and popular class of ensemble learning techniques. Historically, boosting algorithms were challenging to implement, and it was not until AdaBoost demonstrated how to implement boosting that the technique could be used effectively. AdaBoost and modern gradient boosting work by sequentially adding models that correct the residual prediction errors of the model. As such, boosting methods are known to be effective, but constructing models can be slow, especially for large datasets. More recently, extensions designed for computational […]

An ensemble learning method involves combining the predictions from multiple contributing models. Nevertheless, not all techniques that make use of multiple machine learning models are ensemble learning algorithms. It is common to divide a prediction problem into subproblems. For example, some problems naturally subdivide into independent but related subproblems and a machine learning model can be prepared for each. It is less clear whether these represent examples of ensemble learning, although we might distinguish these methods from ensembles given the […]

Ensemble learning combines the predictions from machine learning models for classification and regression. We pursue using ensemble methods to achieve improved predictive performance, and it is this improvement over any of the contributing models that defines whether an ensemble is good or not. A property that is present in a good ensemble is the diversity of the predictions made by contributing models. Diversity is a slippery concept as it has not been precisely defined; nevertheless, it provides a useful practical […]

Bootstrap aggregation, or bagging, is a popular ensemble method that fits a decision tree on different bootstrap samples of the training dataset. It is simple to implement and effective on a wide range of problems, and importantly, modest extensions to the technique result in ensemble methods that are among some of the most powerful techniques, like random forest, that perform well on a wide range of predictive modeling problems. As such, we can generalize the bagging method to a framework […]