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Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as in traditional boosting. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are typically simple ...
LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [4] [5] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and ...
XGBoost works as Newton–Raphson in function space unlike gradient boosting that works as gradient descent in function space, a second order Taylor approximation is used in the loss function to make the connection to Newton–Raphson method. A generic unregularized XGBoost algorithm is:
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
Initially, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner. [3] Algorithms that achieve this quickly became known as "boosting". Freund and Schapire's arcing (Adapt[at]ive Resampling and Combining), [7] as a general technique, is more or less synonymous with boosting. [8]
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]
Boosting methods have close ties to the gradient descent methods described above can be regarded as a boosting method based on the loss: L 2 Boost. [ 3 ] Validation-based early stopping
Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through ...