Stacked generalization, or stacking, may be a less popular machine learning ensemble given that it describes a framework more than […]

Stacked generalization, or stacking, may be a less popular machine learning ensemble given that it describes a framework more than […]
Ensemble methods involve combining the predictions from multiple models. The combination of the predictions is a central part of the […]
Ensemble learning is a general meta approach to machine learning that seeks better predictive performance by combining the predictions from […]
Gradient boosting is an ensemble of decision trees algorithms. It may be one of the most popular techniques for structured […]
Occam’s razor suggests that in machine learning, we should prefer simpler models with fewer coefficients over complex models like ensembles. […]
Meta-learning in machine learning refers to learning algorithms that learn from other learning algorithms. Most commonly, this means the use […]
Dynamic classifier selection is a type of ensemble learning algorithm for classification predictive modeling. The technique involves fitting multiple machine […]
Blending is an ensemble machine learning algorithm. It is a colloquial name for stacked generalization or stacking ensemble where instead […]
The XGBoost library provides an efficient implementation of gradient boosting that can be configured to train random forest ensembles. Random […]
Light Gradient Boosted Machine, or LightGBM for short, is an open-source library that provides an efficient and effective implementation of […]