Sentence examples for data for model training from inspiring English sources

The inclusion of inactive data for model training produces models with superior AUC and improved early recognition capabilities, although the results from internal and external validation of the models show differing performance between the breadth of models.

The artificial neural network technique has been widely investigated, but it requires a large amount of historical data for model training and suffers from local optima and overfitting issues.

Given the benefit of both domain-based extrapolation and using plasmodial DHFR bioactivity data for model training, all plasmodial DHFR data were included in the training set for further MoA prediction of the GSK TCAMS phenotypic dataset in order to optimize recall values.

These results suggest that the use of historical occurrence data for model training can improve performance, at least in the native range, but the magnitude of this effect is dependent on the degree to which modern and historical ranges for each species differ.

Resource allocation strategies were applied to generating phenotypic data for model training.

Another practical consideration is the type of data available for model training.

A self-consistency test is a method where the performance of a model is evaluated according to the same data used for model training.

To generate the data set for model training, we randomly selected a number of non-cases to match the number of cases in the training data set (see Table 1 for the definitions of cases and non-cases).

Their performance depends directly on the data design chosen for model training and validation.

This means that when the Sln1 data is used for model training, we estimate the Sho1 branch parameters with a very high uncertainty with a median bias of 31 % and 33%%, respectively.

More specifically, two data sets are chosen for model training, and the remaining one is for model testing.