Sentence examples for predictors selection from inspiring English sources

Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting.

Creation of an optimal predictive model demands many critical and time-consuming steps, including data collection and processing, appropriate data representation (descriptors and fingerprints calculation), evaluation of the data set modelability, best predictors selection, machine learning models fitting and validation.

In ASD, the significantly correlated climate variables were subjected to backward stepwise predictor selection and the selected predictors were subsequently used to estimate the selected low flow indices using Multiple Linear Regression.

Another explanation for the observed differences in selected predictors might be found in the methodological limitations of predictor selection in statistically derived models.

Predictor selection varied from watershed to watershed, and the common variables that were selected were not always selected in the same order.

Variable selection was performed through the correlation coefficients followed by the ordered predictor selection (OPS) algorithm.

A set of E-Dragon descriptors (1,666) were submitted to a variable selection through Ordered Predictor Selection (OPS) algorithm.

The potential benefits of using wavelet components as predictors in DSM may be further revealed in the future when more predictor selection approaches and mapping methods are considered.

This variable indicator suite was combined with an ordered predictor selection (OPS) method to choose the optimum number of spectral variables (NSV).

Variable selection was based on the genetic algorithm (GA) and ordered predictor selection (OPS) approaches, and the models were built using the multiple linear regression (MLR) and partial least squares (PLS) regression methods.

A stepwise predictor selection procedure to design ordinary least squares (OLS) regression models allowed quite good modeling of the sensory attributes with no more than four morphometrical descriptors (adjusted R2 ⩾ 0.9).