Sentence examples for model generation validation from inspiring English sources

This database includes reports of model generation, validation and prediction according to the OECD standards.

The technology and background that allowed 3-D methodologies to be economically applied to the tens of millions of chemical structures in the PubChem Compound database are described elsewhere [5 10] covering various aspects of the project, including conformer model generation validation [6], the relative uniqueness of molecular shape [7], and 3-D neighboring methodology [8].

To guard against chance correlation, Y-scrambling was performed by randomly shuffling the activities within the training set and repeating the model generation and validation steps.

The optimum number of PCs was determined through an iterative process of chemometric model generation and validation.

In order to move beyond the phase of cataloguing the parts list and truly transform data into knowledge, and knowledge into principles, iterative cycles of data collection and model generation and validation will be necessary.

The accuracies for each of these models during model generation and cross validation are listed (Table 3).

As far as we know, this is the first study to use robust modeling techniques for model generation, followed by external validation to generate and validate symptom-based predictive models of endometriosis in a large prospectively recruited cohort of women across different countries and ethnicities.

The methods used during the creation of the context model can be grouped into three major parts: a factor identification phase, a model generation phase and a model validation phase (see Figure 2).

Furthermore, we integrate this approach in the rxncon framework to support automatic model generation and iterative model definition and validation.

Taken together, we (i) present a bipartite Boolean modelling approach that retains contextual activation information, (ii) provide software support for automatic model generation, visualisation and simulation, and (iii) demonstrate its use for iterative model generation and validation.

Classification algorithms including Naïve Bayes, Random Forest, J48 and SMO with 10 fold cross-validation and default parameters were used for model generation on these data sets.