Sentence examples for variables bag from inspiring English sources

The base categories are the binary variables BAG, ({rm Condition}^{{rm WITHOUT}}), and COMPROP.

The sets of induced rules were used to construct component classifiers in variable consistency bagging [ 28, 29].

Its source is shown using three binary variables (BANK, COURT and BAG) that take on the value of 1 if the report stems from the respective source.

The majority of the variation associated with the Her2/neu, ER, PR, Bcl-2 and Bag-1 variables can be captured by the most dominant principal components.

For a given decision tree, a random subset of the data with identifying true class information is selected to build the tree ("bootstrap sample" or "training set"), and then the remaining data, the "out-of-bag" (OOB) variables, are passed down the tree to obtain a class prediction for each sample.

Using the out-of-bag variable measure, we identified H3K4me3 as the most informative mark required to predict promoters, followed by H3K4me2 and H3K4me1.

To determine which acetylations are the most informative and whether these are robust across cell types, we computed out-of-bag variable importance for acetylations.

To identify the specific histone acetylation marks contributing most to the accurate classification of promoters and enhancers, we computed the out-of-bag variable importance (Bylander 2002; Rajagopal et al. 2013) for each acetylation.

We collected observational and questionnaire measures of variables that predicted free plastic bag usage during the intervention.

The Random Forest (RF) module of R monotonously increases the number of active variables until the out-of-bag (OOB) error no longer decreases.

There are two features involved in the RF algorithm that deserve further attention: the out of bag samples, and the variable importance.