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CatBoost [6] is an open-source software library developed by Yandex. It provides a gradient boosting framework which, among other features, attempts to solve for categorical features using a permutation-driven alternative to the classical algorithm. [ 7 ]
LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft.
Friedman introduced his regression technique as a "Gradient Boosting Machine" (GBM). [4] Mason, Baxter et al. described the generalized abstract class of algorithms as "functional gradient boosting". [ 5 ] [ 6 ] Friedman et al. describe an advancement of gradient boosted models as Multiple Additive Regression Trees (MART); [ 18 ] Elith et al ...
Soon after, the Python and R packages were built, and XGBoost now has package implementations for Java, Scala, Julia, Perl, and other languages. This brought the library to more developers and contributed to its popularity among the Kaggle community, where it has been used for a large number of competitions.
scikit-learn, an open source machine learning library for Python; Orange, a free data mining software suite, module Orange.ensemble; Weka is a machine learning set of tools that offers variate implementations of boosting algorithms like AdaBoost and LogitBoost
Mixed-data sampling (MIDAS) is an econometric regression developed by Eric Ghysels with several co-authors. There is now a substantial literature on MIDAS regressions and their applications, including Ghysels, Santa-Clara and Valkanov (2006), [ 1 ] Ghysels, Sinko and Valkanov, [ 2 ] Andreou, Ghysels and Kourtellos (2010) [ 3 ] and Andreou ...
In statistics, Mallows's, [1] [2] named for Colin Lingwood Mallows, is used to assess the fit of a regression model that has been estimated using ordinary least squares.It is applied in the context of model selection, where a number of predictor variables are available for predicting some outcome, and the goal is to find the best model involving a subset of these predictors.
The code for these "modified" Taylor diagrams was developed, and is available in, Python [13]. A further variant to account for the prediction bias is given by the so called 'solar diagram' (see, Wadoux et al., 2022 [ 18 ] ).