Sentence examples for built using a forward from inspiring English sources
The final multivariable model was built using a forward selection process until all variables with a significant 95% credible interval were included.
Then, based on the amino acid substitution position variables that were significant in the initial screening a final model was built using a forward stepwise regression procedure with a 5% significance level as the variable entry or deletion criterion.
Multivariate models were built using a forward stepwise approach, adding each factor significant at the level of 0.1 and other factors defined in the literature as prognostic in the model one by one.
A multiple linear regression breakpoint predictor was built using a forward feature selection procedure, whereby we iteratively add to the predictor the feature that yields the largest accuracy improvement, until no further addition is beneficial.
After the LR model has been built using a forward stepwise selection procedure for the choice of a subset of predictor variables x i (i = 1, 2,..., d), each continuous predictor is categorized using a locally weighted scatterplot procedure to subjectively identify cut-off points on the basis of training data.
The multivariate models were built using a forward-step approach, the final model included covariates that remained significant at the 0.20 level.
Multivariate models were built using a stepwise forward selection with a significance level of 0.05.
The model was built using a purely forward selection routine, by trying all possible combinations introducing one covariate at each time, and subsequently introducing interactions between the covariates included.
The analysis was carried out using a logistic binary regression model, with PPH as the outcome variable and built using manual forward selection (with p < 0.05 as the cut-off).
A logistic regression multivariate model was built using the forward modeling method.
Their effort models were built using Forward Stepwise Regression (SWR) and they found that cross-company predictions were significantly worse than single-company predictions.
