Results with tuned random forest (TRF). Additional file 4 shows the results of the comparison study between LR, RF and TRF based on the 67 datasets from biosciences/medicine. (PDF 224 kb
Figure S1 Overall success rate of the prediction of tumor types by random forests depending on (a) t...
Random forest (RF) is a widely used machine learning method that shows competitive prediction perfor...
Provides a forest plot of OR estimates and CIs under different modelling assumptions for the variabl...
Datasets from biosciences/medicine. Additional file 2 presents the modified versions of Figs. 3, 5 a...
Additional results of subgroup analyses. Additional file 1 extends Fig. 5 for all considered measure...
Results on partial dependence. Additional file 3 includes a study on interesting extreme cases that ...
Abstract Background and goal The Random Forest (RF) algorithm for regression and classification has ...
Random forest usage in papers. A summary table of papers referencing random forest over a seven mont...
Additional file 1: Appendix: Figure 1. Random forest algorithm for prediction. Figure 2. Decision tr...
BACKGROUND AND GOAL The Random Forest (RF) algorithm for regression and classification has considera...
Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kap...
A comparative analysis of two forest-based regression algorithms is an in-depth investigation of the...
Random forests perform regression using decision trees. It is a non parametric machine learning meth...
Additional file 2: Table S1. Standard deviation of replicate affinity measurements (IC50/EC50/Ki/Kd)...
Intra-parameter values display variation for high p/n studies (combined validation data). The parame...
Figure S1 Overall success rate of the prediction of tumor types by random forests depending on (a) t...
Random forest (RF) is a widely used machine learning method that shows competitive prediction perfor...
Provides a forest plot of OR estimates and CIs under different modelling assumptions for the variabl...
Datasets from biosciences/medicine. Additional file 2 presents the modified versions of Figs. 3, 5 a...
Additional results of subgroup analyses. Additional file 1 extends Fig. 5 for all considered measure...
Results on partial dependence. Additional file 3 includes a study on interesting extreme cases that ...
Abstract Background and goal The Random Forest (RF) algorithm for regression and classification has ...
Random forest usage in papers. A summary table of papers referencing random forest over a seven mont...
Additional file 1: Appendix: Figure 1. Random forest algorithm for prediction. Figure 2. Decision tr...
BACKGROUND AND GOAL The Random Forest (RF) algorithm for regression and classification has considera...
Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kap...
A comparative analysis of two forest-based regression algorithms is an in-depth investigation of the...
Random forests perform regression using decision trees. It is a non parametric machine learning meth...
Additional file 2: Table S1. Standard deviation of replicate affinity measurements (IC50/EC50/Ki/Kd)...
Intra-parameter values display variation for high p/n studies (combined validation data). The parame...
Figure S1 Overall success rate of the prediction of tumor types by random forests depending on (a) t...
Random forest (RF) is a widely used machine learning method that shows competitive prediction perfor...
Provides a forest plot of OR estimates and CIs under different modelling assumptions for the variabl...
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