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Everything about Random forest
Tree-based ensemble machine learning methods
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. For classification tasks, the output of the random forest is the class selected by most trees. For regression tasks, the output is the average of the predictions of the trees. Random forests correct for decision trees' habit of overfitting to their training set. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Random forestA 5-minute orientation
- Skim a recent paper: Random Forest Algorithm OverviewInfluential work since 2022
- Pick up a classic book: Adventures of Robin HoodThe most-reprinted book on the subject
- Read the landmark paper: Random ForestsCited 125,811 times
- Go sideways: Bootstrap aggregatingA closely related topic
- Go sideways: Gradient boostingA closely related topic
📖 Definitions
Noun A structure composed of a collection of decision trees with controlled variance
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Random Forests
Random forest in remote sensing: A review of applications and future directions
The random subspace method for constructing decision forests
RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY
A random forest guided tour
Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
ranger : A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Bias in random forest variable importance measures: Illustrations, sources and a solution
🆕 Influential since 2022
Random Forest Algorithm Overview
A review on longitudinal data analysis with random forest
An improved random forest based on the classification accuracy and correlation measurement of decision trees
Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)
Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series
📚 Books worth reading
Adventures of Robin HoodEleanor Graham Vance, Eleanor Graham Vance · 1953
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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