Results on partial dependence. Additional file 3 includes a study on interesting extreme cases that allows to gain more insight into the behaviour of LR and RF using partial dependence plots defined in “Partial dependence plots” section. (PDF 256 kb
<p>Forest plot showing the pooled HR of p-mTOR from random-effects model for overall survival by uni...
Appendix: Deducing whether c-equivalence had same bias-reducing potential under logistic regression ...
Table S3. Area under the curve in the training data for logistic regression and five machine learnin...
Results with tuned random forest (TRF). Additional file 4 shows the results of the comparison study...
Additional results of subgroup analyses. Additional file 1 extends Fig. 5 for all considered measure...
Datasets from biosciences/medicine. Additional file 2 presents the modified versions of Figs. 3, 5 a...
Provides a forest plot of OR estimates and CIs under different modelling assumptions for the variabl...
Additional file 1: Appendix: Figure 1. Random forest algorithm for prediction. Figure 2. Decision tr...
Random forest usage in papers. A summary table of papers referencing random forest over a seven mont...
Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kap...
Random forests perform regression using decision trees. It is a non parametric machine learning meth...
Intra-parameter values display variation for high p/n studies (combined validation data). The parame...
Abstract Background and goal The Random Forest (RF) algorithm for regression and classification has ...
Provides a table of description of selected variables collected in the 45 and Up Study baseline surv...
Supplementary Material: Description of Supplementary Files. We include 22 files as supplementary mat...
<p>Forest plot showing the pooled HR of p-mTOR from random-effects model for overall survival by uni...
Appendix: Deducing whether c-equivalence had same bias-reducing potential under logistic regression ...
Table S3. Area under the curve in the training data for logistic regression and five machine learnin...
Results with tuned random forest (TRF). Additional file 4 shows the results of the comparison study...
Additional results of subgroup analyses. Additional file 1 extends Fig. 5 for all considered measure...
Datasets from biosciences/medicine. Additional file 2 presents the modified versions of Figs. 3, 5 a...
Provides a forest plot of OR estimates and CIs under different modelling assumptions for the variabl...
Additional file 1: Appendix: Figure 1. Random forest algorithm for prediction. Figure 2. Decision tr...
Random forest usage in papers. A summary table of papers referencing random forest over a seven mont...
Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kap...
Random forests perform regression using decision trees. It is a non parametric machine learning meth...
Intra-parameter values display variation for high p/n studies (combined validation data). The parame...
Abstract Background and goal The Random Forest (RF) algorithm for regression and classification has ...
Provides a table of description of selected variables collected in the 45 and Up Study baseline surv...
Supplementary Material: Description of Supplementary Files. We include 22 files as supplementary mat...
<p>Forest plot showing the pooled HR of p-mTOR from random-effects model for overall survival by uni...
Appendix: Deducing whether c-equivalence had same bias-reducing potential under logistic regression ...
Table S3. Area under the curve in the training data for logistic regression and five machine learnin...
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