Sentence examples for performance of the models when from inspiring English sources
The bottleneck problem can be addressed by comparing the performance of the models when the key terms were chosen by the automated process with performance when the terms were chosen by the human experts.
Given that both values reflect the relative predictive performance of the models when using unknown data (e.g. Burnham and Anderson 2002; Bishop 2006), we use these values as an indicator of the relative quality of the two models.
The impact of rescaling VAS values at the level of the individual versus at aggregate level had minimal impact on the performance of the models when compared to models based on the raw VAS values.
This decrease can be explained by a poorer performance of the models when using the 1954 land use map, due to low values of landslide probability occurrence (stable LS classes) in SU affected by slope failures triggered by the 2009 event.
Using a series of Monte Carlo experiments, we demonstrate how extending a geographically weighted likelihood regression (GWLR) model to account for temporal heterogeneity can improve the performance of the model when heterogeneity exists in the spatial and temporal dimensions.
Here, we test the performance of the model when trained on different numbers of sequences.
It is also important to carefully evaluate the performance of the model when simulating the vertical stratification of the atmosphere, which controls the dry deposition process.
This was also done to assess the performance of the model when no values of soil depth can be obtained and an assumption regarding the unknown soil depth has to be done.
In all panels the solid red line is the ROC curve associated with the performance of the model when using the first experimental condition.
The ideal or theoretically optimal performance of the model is denoted as A ≡ Ā, i.e., the performance of the model when sizes of both the training set and the test set are infinity.
It will be crucial to test the performance of the model when alternative assumptions are made and the model cannot be considered to have been fully tested until this step is done.
