Book ChapterDOI
New machine learning algorithm: random forest
Yanli Liu,Yourong Wang,Jian Zhang +2 more
- pp 246-252
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TLDR
This Paper gives an introduction of Random Forest, a new Machine Learning Algorithm and a new combination Algorithm that has been wildly used in classification and prediction, and used in regression too.Abstract:
This Paper gives an introduction of Random Forest. Random Forest is a new Machine Learning Algorithm and a new combination Algorithm. Random Forest is a combination of a series of tree structure classifiers. Random Forest has many good characters. Random Forest has been wildly used in classification and prediction, and used in regression too. Compared with the traditional algorithms Random Forest has many good virtues. Therefore the scope of application of Random Forest is very extensive.read more
Citations
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Proceedings ArticleDOI
Mining Twitter for Insights into ChatGPT Sentiment: A Machine Learning Approach
Shivam Sharma,Rajesh Kumar +1 more
TL;DR: In this article , the authors used sentiment analysis techniques to assess the sentiment of tweets regarding ChatGPT and found that the majority of tweets related to chatGPT are neutral, while a smaller proportion are positive or negative.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
Proceedings Article
Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
Journal ArticleDOI
The random subspace method for constructing decision forests
TL;DR: A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.