Random subspace ensembles consist of the same model fit on different randomly selected groups of input features (columns) in the training dataset. There are many ways to choose groups of features in the training dataset, and feature selection is a popular class of data preparation techniques designed specifically for this purpose. The features selected by different configurations of the same feature selection method and different feature selection methods entirely can be used as the basis for ensemble learning. In this […]

Extreme Gradient Boosting (XGBoost) is an open-source library that provides an efficient and effective implementation of the gradient boosting algorithm. Although other open-source implementations of the approach existed before XGBoost, the release of XGBoost appeared to unleash the power of the technique and made the applied machine learning community take notice of gradient boosting more generally. Shortly after its development and initial release, XGBoost became the go-to method and often the key component in winning solutions for classification and regression […]

Light Gradient Boosted Machine, or LightGBM for short, is an open-source library that provides an efficient and effective implementation of the gradient boosting algorithm. LightGBM extends the gradient boosting algorithm by adding a type of automatic feature selection as well as focusing on boosting examples with larger gradients. This can result in a dramatic speedup of training and improved predictive performance. As such, LightGBM has become a de facto algorithm for machine learning competitions when working with tabular data for […]

The XGBoost library provides an efficient implementation of gradient boosting that can be configured to train random forest ensembles. Random forest is a simpler algorithm than gradient boosting. The XGBoost library allows the models to be trained in a way that repurposes and harnesses the computational efficiencies implemented in the library for training random forest models. In this tutorial, you will discover how to use the XGBoost library to develop random forest ensembles. After completing this tutorial, you will know: […]

Blending is an ensemble machine learning algorithm. It is a colloquial name for stacked generalization or stacking ensemble where instead of fitting the meta-model on out-of-fold predictions made by the base model, it is fit on predictions made on a holdout dataset. Blending was used to describe stacking models that combined many hundreds of predictive models by competitors in the $1M Netflix machine learning competition, and as such, remains a popular technique and name for stacking in competitive machine learning […]