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Everything about Bootstrap aggregating
Method in machine learning
Bootstrap aggregating, also called bagging or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms. It also reduces variance and overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the ensemble averaging approach. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Bootstrap aggregatingA 5-minute orientation
- Skim a recent paper: Modeling the organic matter of water using the decision tree coupled with bootstrap aggregated and least-squares boostingInfluential work since 2022
- Pick up a classic book: The personal MBAThe most-reprinted book on the subject
- Read the landmark paper: Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregatingCited 257 times
- Go sideways: Random forestA closely related topic
- Go sideways: Out-of-bag errorA closely related topic
📖 Definitions
Noun A machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression.
🏆 Landmark papers (most cited)
Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating
Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating
Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating
Wind power prediction using bootstrap aggregating trees approach to enabling sustainable wind power integration in a smart grid
Optimal scheduling of electrical power in energy-deficient scenarios using artificial neural network and Bootstrap aggregating
A Bootstrap Framework for Aggregating within and between Feature Selection Methods
Reduced bootstrap aggregating of learning algorithms
Multi-agent evolution reinforcement learning method for machining parameters optimization based on bootstrap aggregating graph attention network simulated environment
🆕 Influential since 2022
Modeling the organic matter of water using the decision tree coupled with bootstrap aggregated and least-squares boosting
Enhancing landslide susceptibility mapping through advanced hybridization of bootstrap aggregating based decision tree algorithms
Efficient gene–environment interaction testing through bootstrap aggregating
A Bootstrap-Aggregating in Random Forest Model for Classification of Corn Plant Diseases and Pests
Tourism Demand Forecasting in Normal and Crisis Times: Combining Bootstrap-Aggregating and Bayesian Approaches
📚 Books worth reading
The personal MBAJosh Kaufman · 2010
TCP/IP IllustratedW. Richard Stevens, Kevin R. Fall · 1993
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Random forest Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude…
- Out-of-bag error Out-of-bag (OOB) error, also called out-of-bag estimate, is a method of measuring the prediction error of random forests, boosted decision trees, and other…
- Ensemble learning In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from…
- Resampling (statistics) In statistics, resampling is the creation of new samples based on one observed sample. Resampling methods are: Permutation tests (also re-randomization…
- Decision tree learning Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or regression…
- Boosting (machine learning) In machine learning (ML), boosting is an ensemble learning method that combines a set of less accurate models (called "weak learners") to create a single…
- Multi-label classification In machine learning, multi-label classification or multi-output classification is a variant of the classification problem where multiple nonexclusive labels…
- Random subspace method In machine learning the random subspace method, also called attribute bagging or feature bagging, is an ensemble learning method that attempts to reduce…