Author Archive | Jason Brownlee

A Gentle Introduction to Multiple-Model Machine Learning

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 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 […]

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Essence of Boosting Ensembles for Machine Learning

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 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. […]

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Box and Whisker Plots of Classification Accuracy for Standalone Machine Learning Models

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 available models or pruning members from a fully defined ensemble. The goal is often to reduce the model or computational complexity of an ensemble with little or no effect on the performance of an ensemble, […]

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Box and Whisker Plots of Accuracy Distributions for k Values in KNORA-U

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 prediction. The technique involves fitting multiple machine learning models on the training dataset, then selecting the models that are expected to perform best when making a prediction for a specific new example, based on the […]

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How to Combine Predictions for Ensemble Learning

How to Combine Predictions for Ensemble Learning

Ensemble methods involve combining the predictions from multiple models. The combination of the predictions is a central part of the ensemble method and depends heavily on the types of models that contribute to the ensemble and the type of prediction problem that is being modeled, such as a classification or regression. Nevertheless, there are common […]

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