Plant Phenomics / 2021 / Article / Tab 2

Research Article

A Comparative Analysis of Quantitative Metrics of Root Architecture

Table 2

Results of regression models created with random forest. The values of random forest model with entire set of variables and those with only most important variables are presented for the bean and maize aggregate phene metrics.

Aggregate phenotypic metric (% variance explained)
BeanMaize
Model with all variablesModel with most important variablesModel with all variablesModel with most important variables

Total length89.591.68285
Total area87877881
Total volume81.788.57981.6
Volume distribution87916166
Max no. of roots78.8846772.8
Median no. of roots79.9877175
Bushiness62673641
Max depth98.699.67984
Max width91909599
Convex hull area97.8979093.4
Convex hull volume97.697.68789.9
Ellipse minor axis94.993.68085
Ellipse major axis96.797.39598.6
Ellipse aspect ratio85.987.451.962
Solidity97.497.58989
FD67681620
FA93.594.98890

Random forest possesses its own reliable statistical characteristics, which could be used for validation and model selection. The major criterion for estimation of internal predictive ability of the random forest models and model selection is the value of . in random forest is interpreted as a measure of predictive quality of random forest model on independent samples. Random forest models were run with the aggregate phenotype as dependent variable and all the phenes as predictor variables. Most important variables were chosen based on the % increase in mean square, and random forest models were run with only the most important variables.