Sentence examples for predictor feature from inspiring English sources

Instead of using the k-NNs solely based on the predictor feature space, those NNs which were of a different dominant species than the target plot were not considered in the predictions.

Sigma denotes the bandwidth of the Gaussian kernel in units of distance in predictor feature space from the queried patient's predictor to the furthest predictor among the rest of the patients.

Each coefficient in the elastic-net regression measures the partial correlation between the predictor feature (e.g. CNV, gene expression, methylation level or mutation status) and the outcome (e.g. clinical stage).

The loss function of Lasso regression is defined as: (1) where x ip denotes the pth predictor (feature) in the ith data point, y i denotes the value of the response for this data point, and β p denotes the regression coefficient of the pth feature.

The predictor features extracted for the study (Table 2) were selected based on the earlier studies (e.g., Vauhkonen et al. 2014b).

Similar to the results obtained previously in this study, the combination of the predictor features differed depending on the dominant species in question.

This is attributed to the ability of hybrid RBF-PLS model to capture any underlying non-linearity in the relations between the predictor features and the response variables.

We derived predictor features from airborne laser scanning (ALS) data and used Most Similar Neighbor (MSN) and Seemingly Unrelated Regression (SUR) as examples of non-parametric and parametric prediction methods, respectively.

Most of these approaches are aimed at UV in large vocabulary tasks, that is, posterior probability estimation using word lattices and predictor features like acoustic stability and hypothesis density.

The relationships between the predictor features derived from the ALS data and the volumes of Scots pine, Norway spruce, and deciduous species were considerably different depending on the dominant species.

Firstly, "area-based" methods where the area-based prediction of forest variables is based on the statistical dependency between variables measured in field plots and the predictor features derived from LiDAR data (e.g. Corona and Fattorini 2008; White et al. 2013).