Paola Zuccolotto

TL;DR: An alternative selection method, based on the technique of Random Forests, is proposed in the context of classification, with an application to a real dataset.

TL;DR: In this paper, a measure of variable importance is defined as the total heterogeneity reduction produced by a given covariate on the response variable when the sample space is recursively partitioned.

TL;DR: The first genome-wide transcriptional analysis of 120 RNA samples corresponding to 10 Italian grapevine varieties collected at four growth stages revealed greater variation among the red cultivars than between red and white cultivars at the transcriptome level, suggesting that anthocyanin accumulation during berry maturation has a direct impact on the transcriptomic regulation of multiple biological processes.

TL;DR: In this paper it is shown how interesting information useful for marketing management can be obtained by combining the results from cub models and algorithmic data mining techniques (specifically, variable importance measurements from Random Forest).

TL;DR: The mutual relationships between genotypic and environmental variables during G×E interaction in a woody perennial species is unraveled, providing a reference model to explore how cultivated fruit crops respond to diverse environments.