Application of Breiman's Random Forest to Modeling Structure-Activity Relationships of Pharmaceutical Molecules
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Summary
The performance of Random Forest with default settings on six publicly available data sets is already as good or better than that of three other prominent QSAR methods: Decision Tree, Partial Least Squares, and Support Vector Machine.
- Published
- 2004-06-09
- Cited by
- 281
- References
- 15
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3705703
References
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- Random Forests
- Overfitting in Making Comparisons Between Variable Selection Methods
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- Sélection de variables par les machines à vecteurs supports pour la discrimination binaire et multiclasse en grande dimension
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- Random Forest and PCA for Self-Organizing Maps based Automatic Music Genre Discrimination
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- Methods for automation of vascular lesions detection in computed tomography images
- Novel machine learning and correlation network methods for genomic data
- Multiclass cancer classfication and gene selection using mutation information
- Approximate False Positive Rate Control in Selection Frequency for Random Forest
- Quantitative Analysis of Narrative Reports of Psychedelic Drugs
- Remote sensing of forest health : the detection and mapping of Pinus patula trees infested by Sirex noctilio.
- Gene selection and classification of microarray data using random forest
- Multi-level classification of emotional body expression
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