Sentence examples for in a classification setting from inspiring English sources
In this commentary it has been noted that, when ligand features are represented by a string of binary numbers, one must end up with a linear model for describing the dependency (if any) between the chemical structure of a ligand and its bioactivity of interest albeit in a classification setting.
The way ICP works in a classification setting is fairly simple.
In particular, supervised machine learning methods use the existing known interactions as training data and formulate the interaction prediction problem in a classification setting, with target classes: 'interacting' or 'non-interacting'.
In particular, supervised machine learning based methods use the few experimentally discovered interactions as training data and formulate the interaction prediction problem in a classification setting, with target classes: 'interacting' or 'non-interacting'.
Even though this top 100 is generally too small to capture the complexity of event extraction in a classification setting, analysis of the most frequently occurring features in the top 100 provides strong clues of the most discriminating features and allows us to learn interesting aspects of the feature generation process.
From Figure 8, it can be seen that method M1 achieved best performance compared to other methods in a multilabel classification setting at different training data ratios.
The study also highlights a common problem of diagnostic/classification criteria: In the classification setting a higher sensitivity will allow more patients with true RA to go into early studies, accepting the fact that some will be included falsely.
She began competing at the international level in 2010 with an SM10 classification, setting world records in the process (one of which she broke again in qualifying for the 2012 Paralympics).
The first type, which we call a "classification set," was collected in the seven rooms of the laboratory site.
In a supervised classification setting, STMs improved performance in all our experimental settings, also in comparison with the equivalent standard (non-scale-aware) measures.
As the models were trained with continuous ln EC50 values but largely applied in a binary classification setting, we tested if training of the models as pure binary classifiers offered any advantages.
