Additional file 1: Appendix: Figure 1. Random forest algorithm for prediction. Figure 2. Decision tree construction and prediction from randomforest algorithm
Figure S2. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S4. Receiver operating characteristic curves for the prediction of SGA among multiparous wome...
<div><p>Statistical models to predict incident diabetes are often based on limited variables. Here w...
Random forests perform regression using decision trees. It is a non parametric machine learning meth...
Datasets from biosciences/medicine. Additional file 2 presents the modified versions of Figs. 3, 5 a...
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...
Table S2. Training parameter grids and parameters used for five machine learning methods for the pre...
Figure S8. Receiver operating characteristic curves for the prediction of LGA among multiparous wome...
Table S3. Area under the curve in the training data for logistic regression and five machine learnin...
Figure S6. Receiver operating characteristic curves for the prediction of LGA among primiparous wome...
Figure S1 Overall success rate of the prediction of tumor types by random forests depending on (a) t...
Figure S7. Receiver operating characteristic curves for the prediction of LGA among multiparous wome...
Table S1. Predictors of fetal growth abnormalities and their use in the prediction models. (PDF 71 k...
Figure S1. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S2. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S4. Receiver operating characteristic curves for the prediction of SGA among multiparous wome...
<div><p>Statistical models to predict incident diabetes are often based on limited variables. Here w...
Random forests perform regression using decision trees. It is a non parametric machine learning meth...
Datasets from biosciences/medicine. Additional file 2 presents the modified versions of Figs. 3, 5 a...
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...
Table S2. Training parameter grids and parameters used for five machine learning methods for the pre...
Figure S8. Receiver operating characteristic curves for the prediction of LGA among multiparous wome...
Table S3. Area under the curve in the training data for logistic regression and five machine learnin...
Figure S6. Receiver operating characteristic curves for the prediction of LGA among primiparous wome...
Figure S1 Overall success rate of the prediction of tumor types by random forests depending on (a) t...
Figure S7. Receiver operating characteristic curves for the prediction of LGA among multiparous wome...
Table S1. Predictors of fetal growth abnormalities and their use in the prediction models. (PDF 71 k...
Figure S1. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S2. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S4. Receiver operating characteristic curves for the prediction of SGA among multiparous wome...
<div><p>Statistical models to predict incident diabetes are often based on limited variables. Here w...