Sentence examples for based on variable importance from inspiring English sources

Moreover, based on variable importance for the RF-model, we observed that wind erosion, technology development, aridity index, slope index, vegetation state and land-use change variables are relatively most important on desertification hazard of Taybad-Bakharz region, respectively.

The potential variables contributing to the classification were selected based on variable importance in the projection (VIP > 2.0) values.

For example, Diaz-Uriarte and Alvarez de Andrés [ 37] proposed a backward elimination algorithm to select relevant subset of genes based on variable importance.

Since there were many potential features for siRNA classification, random forests were used for feature selection based on variable importance scores.

Boulesteix and Strimmer [ 35] describe and refer the connection of PLS to gene selection based on "variable importance in projection" (VIP) indicator proposed by Musumarra et al. [ 36], which indicates the importance of genes in the used PLS latent components.

Based on variable importance index and correlation analysis, we selected the following five variables in the final models (by ranking of the variable importance index): maximum speed in 4 minutes, maximum speed in 60 minutes, median speed in 30 minutes, maximum distance difference in 6 minutes, and maximum distance difference in 30 minutes.

In this paper, we introduce a variable selection procedure, called the PLSVS method, to screen active effects in mixed-level SSDs based on the variable importance in projection which is an important concept in the partial least-squares regression.

The subsequent predictive models are trained with support vector machines introducing the variables sequentially from a ranked list based on the variable importance.

In model 8, 3,095 variables had a significant contribution to the classification based on the variable importance in the projection (VIP).

Based on Random Forest variable importance values, plantation age was the best predictor of canopy damage.

The dataset was additionally analyzed with the machine-learning algorithm RF in order to address issues of model overfitting and markers were selected based on their variable importance ranking.