Sentence examples for prune variables from inspiring English sources

Performance of this composite model can be further improved using feature selection techniques to prune variables.

Although in classical logistic regression it is possible to prune variables which prove to be of limited use in the overall model it is much harder to do this for the neural network model and the entire historically developed question set is therefore still collected.

One method prunes the input variables by the cross-correlation method, while the other prunes the support vectors according to the derived relationship among SVM parameters.

We first pruned all the variables showing significance in univariate analysis (Table 3) by using all-subset selection with Mallows' Cp optimization criteria, and recognized biomarker panels capable of discriminating basal-like and HER2-overexpressing subtypes at FDR 0.2 level.

Some preliminary benchmarking showed that the reified version is more efficient if the length of the list is greater than 3 or 4. Constraints over finite domains come with domain filtering algorithms which dynamically prune the domain of variables when the domain of other variables change in a constraint.

Many strategic decisions are made during a CART analysis, and the final usefulness of a classification tree depends heavily on the skill and judgment of the statisticians, scientists, and clinicians who determine the input variables and prune the output.

Expression of EcR dominant negative constructs and an EcR inverted repeat construct resulted in pruning defects of larval axon arbors and a lack of filopodia during pruning, but showed variable effects on outgrowth depending on the construct expressed.

Pruning of the correlating variables, often referred to as feature selection, can be achieved by some statistical tools to select input variables that are used in the regression analysis to develop the empirical correlations or by common feature selection techniques such as neighbourhood component analysis (NCA), sequential feature selection and LASSO.

These variables were "pruned" using an automated stepwise procedure for optimizing the Akaike information criterion [24].

Feature selection method is used to select relevant variables, and pruning technique is applied to obtain low information-burden models.

To reduce the number of variables, LD pruning is used on 409,656 SNPs after the single SNP association analysis.