Archive | Ensemble Learning

Multivariate Adaptive Regression Splines (MARS) in Python

Multivariate Adaptive Regression Splines (MARS) in Python

Multivariate Adaptive Regression Splines, or MARS, is an algorithm for complex non-linear regression problems. The algorithm involves finding a set of simple linear functions that in aggregate result in the best predictive performance. In this way, MARS is a type of ensemble of simple linear functions and can achieve good performance on challenging regression problems […]

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Example of Combining Hyperplanes Using an Ensemble

Develop an Intuition for How Ensemble Learning Works

Ensembles are a machine learning method that combine the predictions from multiple models in an effort to achieve better predictive performance. There are many different types of ensembles, although all approaches have two key properties: they require that the contributing models are different so that they make different errors and they combine the predictions in […]

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Box Plot of Random Subspace Ensemble Features vs. Classification Accuracy

How to Develop a Random Subspace Ensemble With Python

Random Subspace Ensemble is a machine learning algorithm that combines the predictions from multiple decision trees trained on different subsets of columns in the training dataset. Randomly varying the columns used to train each contributing member of the ensemble has the effect of introducing diversity into the ensemble and, in turn, can lift performance over […]

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Box and Whisker Plots of Bits Per Class vs. Distribution of Classification Accuracy for ECOC

Error-Correcting Output Codes (ECOC) for Machine Learning

Machine learning algorithms, like logistic regression and support vector machines, are designed for two-class (binary) classification problems. As such, these algorithms must either be modified for multi-class (more than two) classification problems or not used at all. The Error-Correcting Output Codes method is a technique that allows a multi-class classification problem to be reframed as […]

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Why Use Ensemble Learning

Why Use Ensemble Learning?

What are the Benefits of 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 […]

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Ensemble Learning Pattern Classification Using Ensemble Methods

6 Books on Ensemble Learning

Ensemble learning involves combining the predictions from multiple machine learning models. The effect can be both improved predictive performance and lower variance of the predictions made by the model. Ensemble methods are covered in most textbooks on machine learning; nevertheless, there are books dedicated to the topic. In this post, you will discover the top […]

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Box Plot of Gradient Boosting Ensemble Tree Depth vs. Classification Accuracy

How to Develop a Gradient Boosting Machine Ensemble in Python

The Gradient Boosting Machine is a powerful ensemble machine learning algorithm that uses decision trees. Boosting is a general ensemble technique that involves sequentially adding models to the ensemble where subsequent models correct the performance of prior models. AdaBoost was the first algorithm to deliver on the promise of boosting. Gradient boosting is a generalization […]

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Box Plot of AdaBoost Ensemble Weak Learner Depth vs. Classification Accuracy

How to Develop an AdaBoost Ensemble in Python

Boosting is a class of ensemble machine learning algorithms that involve combining the predictions from many weak learners. A weak learner is a model that is very simple, although has some skill on the dataset. Boosting was a theoretical concept long before a practical algorithm could be developed, and the AdaBoost (adaptive boosting) algorithm was […]

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Box Plot of Random Subspace Ensemble Number of Features vs. Classification Accuracy

How to Develop a Bagging Ensemble with Python

Bagging is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is also easy to implement given that it has few key hyperparameters and sensible heuristics for configuring these hyperparameters. Bagging performs well in general and provides the basis for a whole field of ensemble of decision tree algorithms such […]

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