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Using Random Forest to Learn Imbalanced Data

, , and . 666. University of California, Berkeley 110 (1-12): 24, (2004)

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Profiling Diverse Chemical Space to Map the Druggable Proteome., , , , , and . BCB, page 516. ACM, (2018)Correction to Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships., , , , and . J. Chem. Inf. Model., 60 (3): 1910 (2020)Experimental Error, Kurtosis, Activity Cliffs, and Methodology: What Limits the Predictivity of Quantitative Structure-Activity Relationship Models?, , , , , , , , , and . J. Chem. Inf. Model., 60 (4): 1969-1982 (2020)Challenges in Variable Importance Ranking Under Correlation., , , , , , and . CoRR, (2024)Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships., , and . CoRR, (2021)Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction, , and . Ecosystems, (2006)Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships., , , , and . Journal of Chemical Information and Modeling, 56 (12): 2353-2360 (2016)Development and Evaluation of Conformal Prediction Methods for QSAR., , , and . CoRR, (2023)Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules., , , and . Multiple Classifier Systems, volume 3077 of Lecture Notes in Computer Science, page 334-343. Springer, (2004)Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling., , , , , and . Journal of Chemical Information and Modeling, 45 (3): 786-799 (2005)