Sentence examples for classification settings from inspiring English sources

Support vector machine is a supervised, multivariate classification method [37] with optimal empirical performance in many classification settings [38] that has previously been utilized in neuroimaging research [2] [3], [7], [9].

Although there is no quantitative definition of the term "likely", guidance has been proposed in certain variant classification settings.

Hence, detailed knowledge of the entire space of inactive compounds improves performance of the models in binary classification settings.

We created a modular evaluation framework to gain insight in different classification settings and feature relevance rankings.

In two-group discrimination, we achieved approximately balanced sensitivity and specificity across six classification settings by properly assigning higher weight to the PGx variant group.

With all different classification settings, we performed a leave-one-out cross-validation: each item in the training set is classified with a model built with the rest of the training set items.

Table 4 Machine learning algorithms used and a short description of their training parameters Classifier Classification scheme Settings Sequential Minimal Optimization (SMO) functions The complexity parameter was set at 1, the epsilon for a round-off error was 1.0 E-12, and the option of normalizing training data was chosen.

The default annotation category for biological processes of GO (GOTERM_BP_FAT) and the medium classification stringency settings were selected.

The default annotation category and the high-classification stringency settings were selected.

We collected data on patient demographics, admission diagnosis and severity of illness; study objective, setting, and design; ventilator settings; classification of pleural effusion (exudative vs. transudative); technique of drainage, including the use of imaging guidance, the level of training of the operator, and the type of drainage procedure performed; and outcomes.

We then formulate cross-validation algorithms for model selection and model assessment in classification and regression settings which avoid the pitfalls, and then show results of applying these methods on QSAR datasets.