Algorithms that learn by building decision trees or by combining many weak models, such as ID3, C4.5, AdaBoost and gradient boosting. An algorithm belongs here when its cited article describes a decision tree learner or a boosting or bagging ensemble. It excludes clustering and reinforcement learning methods. A member is placed here by what its own cited article states about it, not by the name of the algorithm or by the uses it is put to today, so each entry sits in the group its source describes it as belonging to.
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Decision Tree and Ensemble Learning Algorithms
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Decision tree learning - Wikipedia
Has Member: Decision tree learning, Lead sentenceQuote, Has Member: Decision tree learning, Lead sentence
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.
Bootstrap aggregating (Wikipedia)
Has Member: Bootstrap aggregating, Lead paragraph
Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms.
Has Member: Bootstrap Aggregating Algorithm, Lead
Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms.
View the SourceID3 Algorithm (Wikipedia)
Has Member: ID3 Algorithm, Lead sectionQuote, Has Member: ID3 Algorithm, Lead section
used to generate a decision tree from a dataset
C4.5 Algorithm (Wikipedia)
Has Member: C4.5 Algorithm, Lead sectionQuote, Has Member: C4.5 Algorithm, Lead section
is an algorithm used to generate a decision tree
Wikipedia: AdaBoost
Has Member: AdaBoost Algorithm, Lead sectionQuote, Has Member: AdaBoost Algorithm, Lead section
AdaBoost (short for Adaptive Boosting)
Wikipedia: Gradient boosting
Has Member: Gradient Boosting Algorithm, Lead sectionQuote, Has Member: Gradient Boosting Algorithm, Lead section
machine learning technique based on boosting in a functional space
Wikipedia: Random Forest Algorithm
Wikimedia FoundationHas Member: Random Forest Algorithm, LeadQuote, Has Member: Random Forest Algorithm, Lead
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training.
scikit-learn: scikit-learn: Decision Trees
scikit-learnHas Member: CART Algorithm, Section 1.10.6Quote, Has Member: CART Algorithm, Section 1.10.6
CART (Classification and Regression Trees) is very similar to C4.5, but it differs in that it supports numerical target variables (regression) and does not compute rule sets.
XGBoost developers: XGBoost Documentation
XGBoost developersHas Member: XGBoost Algorithm, HomeQuote, Has Member: XGBoost Algorithm, Home
XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.
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