Author Archive | Jason Brownlee

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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Bagging Ensemble

A Gentle Introduction to Ensemble Learning Algorithms

Ensemble learning is a general meta approach to machine learning that seeks better predictive performance by combining the predictions from multiple models. Although there are a seemingly unlimited number of ensembles that you can develop for your predictive modeling problem, there are three methods that dominate the field of ensemble learning. So much so, that […]

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What Is a Gradient in Machine Learning?

What Is a Gradient in Machine Learning?

Gradient is a commonly used term in optimization and machine learning. For example, deep learning neural networks are fit using stochastic gradient descent, and many standard optimization algorithms used to fit machine learning algorithms use gradient information. In order to understand what a gradient is, you need to understand what a derivative is from the […]

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Contour Plot of the Test Objective Function With Adadelta Search Results Shown

Gradient Descent With Adadelta from Scratch

Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that it uses the same step size (learning rate) for each input variable. AdaGradn and RMSProp are extensions to gradient descent that add a self-adaptive […]

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What Is Semi-Supervised Learning

What Is Semi-Supervised Learning

Semi-supervised learning is a learning problem that involves a small number of labeled examples and a large number of unlabeled examples. Learning problems of this type are challenging as neither supervised nor unsupervised learning algorithms are able to make effective use of the mixtures of labeled and untellable data. As such, specialized semis-supervised learning algorithms […]

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