Sentence examples for enhanced prediction performance from inspiring English sources

Dimension reduction methods enhanced prediction performance in all data sets, while at the same time computational cost decreased.

Finally, we show how our approach can be used as a building block to obtain Random Forests classification with enhanced prediction performance.

This formulation allows us to model correlations and dependencies between the examined HTS assays and enhance prediction performance.

Here, we demonstrated that providing existing edges could enhance prediction performance especially in the situation of few observations.

Short-term prediction algorithms will be derived for both outcomes based on sFlt-1/PlGF cutoffs and – if found to enhance prediction performance of the model – gestational age.

Gender has been reported to be useful for patient outcome prediction [ 35]; 12-lead ECG changes and bedside qualitative cardiac troponin [ 36] could also be added to the algorithm to enhance prediction performance.

When attempting prediction with only 108 features (i.e., 16% of the information compared to that used in our original study), we found that the morphological changes in culture days 8 10 (D14_ALP model) and days 9 11 (D21_ALP model) are most informative and enhance prediction performance.

For prediction using coding variants, there are further sources of data that could be relevant to enhancing prediction performance.

This suggests that the use of selected subset of features enhances prediction performance by reducing the noise created by the redundant and irrelevant features.

For example, a recent study by Ihemelandu et al (2013) found that preoperative use of the Functional Assessment of Cancer Therapy questionnaire combined with Eastern Cooperative Oncology Group performance allowed enhanced prediction of treatment-associated morbidity and mortality.

These results indicate that combining different individual classifiers trained by balanced subdatasets can effectively enhance the prediction performance for predicting types of J-proteins and deal with the imbalanced data problem.