Sentence examples for for a better interpretability from inspiring English sources

To allow for a better interpretability of the results reached in those Cox regression analyses, for each of the two response variables (OS, DFS) only factors showing a p-value of ≤ 0.1 in the corresponding univariate log-rank analysis were incorporated as independent variables in the according Cox regression models.

We therefore selected (M_{25}) for annotation, as this model has a better interpretability compared to (M_{14}) (more specific topics), with comparable quality of model fit (similar AIC).

Another advantage of feature selection is a better interpretability of a classification model based on lesser features.

This is contrary to other methods such as decision tree or regression, which have a better interpretability (and perform well on binary problems but do not allow non-linear separation).

A continuous age variable was used for all analyses; for better interpretability when displaying results, age was grouped into four categories with equal intervals: 7 to 9, 10 to 12, 13 to 15, and 16 to 18, corresponding roughly to elementary, elementary/early middle school, late middle school/early high school, and upper high school.

This improves the power for detecting perturbations during the onset and the progression of comorbidity, and allows for better interpretability of the dynamics at different time spans.

Therefore, in this study an attempt has been made to determine geomorphological parameters and to study the intercorrelationship (multicollinearity) among variables to screen out the less significant variables out of the analysis and to arrange the remaining into physically significant groups by applying principal component analysis for better interpretability.

22 For better interpretability, we present predicted scores and differences (from observed) that are back transformed to percentages.

Subtypes were also fully characterized for better interpretability, which included the identification of enriched biological pathways, cranial base discriminating features, and, to a more limited extent, correlated clinical traits.

In future work, we plan to develop an interactive UI for the algorithm to enable the user to guide iterative or recursive application of the method to the most interesting topics and leverage ongoing research in the area of topic models to arrive at topics that soften the bag of words assumption and describe topics with sentence phrases for better interpretability.

This algorithm has better interpretability like many other NMF-based methods.