Results 41 to 50 of about 3,398,718 (314)
, 2023 The wealth of data being gathered about humans and their surroundings drives new machine learning applications in various fields. Consequently, more and more often, classifiers are trained using not only numerical data but also complex data objects. For example, multi-omics analyses attempt to combine numerical descriptions with distributions, time ...
Maciej Piernik +2 more
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Random Forest variable importance with missing data [PDF]
, 2012 Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values.
Hapfelmeier, Alexander +2 more
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Random forest classifier performance.
, 2021 Random forest classifier performance.
André Euler (8781737) +8 more
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Fecal source identification using random forest
Microbiome, 2018 Background Clostridiales and Bacteroidales are uniquely adapted to the gut environment and have co-evolved with their hosts resulting in convergent microbiome patterns within mammalian species.
Adélaïde Roguet +3 more
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Implementation of LightGBM and Random Forest in Potential Customer Classification
TIERS Information Technology Journal, 2023 Classification is one of the data mining techniques that can be used to determine potential custumers. Previous research show that the boosting method, especially LGBM, produces the highest accuracy value of all models, namely 100%.
Laura Sari +3 more
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On Oblique Random Forests [PDF]
, 2011 In his original paper on random forests, Breiman proposed two different decision tree ensembles: one generated from "orthogonal" trees with thresholds on individual features in every split, and one from "oblique" trees separating the feature space by randomly oriented hyperplanes.
Bjoern H. Menze +4 more
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Knowledge-Based SystemsThe Random Forest (RF) classifier is often claimed to be relatively well calibrated when compared with other machine learning methods. Moreover, the existing literature suggests that traditional calibration methods, such as isotonic regression, do not substantially enhance the calibration of RF probability estimates unless supplied with extensive ...
Shaker, Mohammad Hossein +1 more
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Pattern Recognition Letters, 2012 In this paper, we introduce a new Random Forest (RF) induction algorithm called Dynamic Random Forest (DRF) which is based on an adaptative tree induction procedure. The main idea is to guide the tree induction so that each tree will complement as much as possible the existing trees in the ensemble.
Bernard, Simon +2 more
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Random Forest Similarity Maps: A scalable visual representation for global and local interpretation
, 2021 This Research work focuses on the interpretation and explainability of Random Forest ensemble model using Visual Analytics.Machine Learning prediction algorithms have made significant contributions in today’s world, leading to increased usage in a ...
Mazumdar, Dipankar
core
SMOTE and Weighted Random Forest for Classification of Areas Based on Health Problems in Java
Journal of Applied Informatics and ComputingRandom Forest (RF) is a popular Machine Learning (ML) approach extensively employed for addressing classification issues. Nevertheless, the RF method for classification problems demonstrates suboptimal performance in cases of data imbalance.
Erwan Setiawan +2 more
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