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XGBoost [2] (eXtreme Gradient Boosting) is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, ...
LightGBM has many of XGBoost's advantages, including sparse optimization, parallel training, multiple loss functions, regularization, bagging, and early stopping. A major difference between the two lies in the construction of trees.
Empirically, it has been found that using small learning rates (such as <) yields dramatic improvements in models' generalization ability over gradient boosting without shrinking (=). [1] However, it comes at the price of increasing computational time both during training and querying : lower learning rate requires more iterations.
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]
Simple classifiers built based on some image feature of the object tend to be weak in categorization performance. Using boosting methods for object categorization is a way to unify the weak classifiers in a special way to boost the overall ability of categorization. [citation needed]
When bootstrap aggregating is performed, two independent sets are created. One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement.
A classification model (classifier or diagnosis [7]) is a mapping of instances between certain classes/groups.Because the classifier or diagnosis result can be an arbitrary real value (continuous output), the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure).
It is shown that this is directly equivalent to decreasing the learning rate in gradient boosting = + (), where decreasing improves the regularization of the boosted classifier. The theory makes it clear that when a learning rate of γ {\displaystyle \gamma } is used, the correct formula for retrieving the posterior probability is now η = f ...