Results 11 to 20 of about 6,285,017 (364)
HML-RF: Hybrid Multi-Label Random Forest
IEEE Access, 2022 Multi-label classification is the supervised learning problem in which an instance is associated with a set of labels. In this, labels are correlated, and hence label dependency information plays a vital role.
Vikas Jain +2 more
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XGBoost and Random Forest Algorithms: An in Depth Analysis
Pakistan Journal of Scientific Research, 2023 Machine learning is playing an increasingly important role in many facets of our lives as technology develops, including forecasting weather, figuring out social media trends, and predicting prices on the world market.
Sana Fatima +4 more
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A random forest guided tour [PDF]
Test (Madrid), 2015 The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general-purpose classification and regression method.
G. Biau, Erwan Scornet
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Generalized random forests [PDF]
The Annals of Statistics, 2019 We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment equations. Following the literature on local maximum likelihood estimation, our method considers a weighted set of nearby ...
Athey, Susan +2 more
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Hyperparameters and tuning strategies for random forest [PDF]
WIREs Data Mining Knowl. Discov., 2018 The random forest (RF) algorithm has several hyperparameters that have to be set by the user, for example, the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn ...
Philipp Probst +2 more
semanticscholar +1 more source
Bioinformatics, 2008 Abstract Although the random forest classification procedure works well in datasets with many features, when the number of features is huge and the percentage of truly informative features is small, such as with DNA microarray data, its performance tends to decline significantly.
Dhammika, Amaratunga +2 more
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Sentiment Analysis With Sarcasm Detection On Politician’s Instagram
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2021 Sarcasm is one of the problem that affect the result of sentiment analysis. According to Maynard and Greenwood (2014), performance of sentiment analysis can be improved when sarcasm also identified. Some research used Naïve Bayes and Random Forest method
Aisyah Muhaddisi +2 more
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Interpreting random forest analysis of ecological models to move from prediction to explanation
Scientific Reports, 2023 As modeling tools and approaches become more advanced, ecological models are becoming more complex. Traditional sensitivity analyses can struggle to identify the nonlinearities and interactions emergent from such complexity, especially across broad ...
Sophia M. Simon +2 more
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A Novel Consistent Random Forest Framework: Bernoulli Random Forests [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2018 Random forests (RFs) are recognized as one type of ensemble learning method and are effective for the most classification and regression tasks. Despite their impressive empirical performance, the theory of RFs has yet been fully proved. Several theoretically guaranteed RF variants have been presented, but their poor practical performance has been ...
Yisen Wang +4 more
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Random forest-based prediction of stroke outcome [PDF]
Scientific Reports, 2021 We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission.
C. Fernandez-Lozano +11 more
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