The phrase "accuracy and interpretability" is correct and usable in written English.
You can use it when discussing the quality of data, models, or analyses, particularly in fields like statistics, machine learning, or research.
Example: "The study emphasizes the importance of accuracy and interpretability in the results to ensure they can be effectively communicated to stakeholders."
Alternatives: "precision and clarity" or "correctness and comprehensibility."
Model accuracy and interpretability are two conflicting objectives, so interpretation preservation during adaptation in data-driven fuzzy system modelling is a challenging task, which has received much attention in fuzzy system modelling community.
Both sets of findings were then reordered and interpreted (ZH and JN) in a framework analysis that accounted for concepts of accuracy and interpretability which had been identified as two core second order concepts across both audiences.
These solutions suffer from issues in accuracy and interpretability.
Consideration of the accuracy and interpretability of these machine vision systems will assist with these challenging malpractice concerns.
The classification accuracy and interpretability of generated rules are of major importance in fuzzy classification systems.
As such it overcomes the trade-off between accuracy and interpretability of soil property predictions.
The experiment results demonstrate that the MFs designed by the proposed method could effectively improve the accuracy, complexity and interpretability of the fuzzy model, which provide helpful information for the fuzzy modeling of BFG pipeline pressure.
Considering the complex nonlinear characteristics of blast furnace gas (BFG) system in steel industry, a MFs learning method based on clustering analysis is proposed in this paper, where a multi-objective density clustering method is reported by combing the targets of the model accuracy, complexity and interpretability.
48 Unfortunately, there is no single "one-size-fits-all" algorithm, integrating accuracy, stability, and interpretability of gene selection.
Incorporating traffic-meteorological interaction information into LURs via dispersion models may improve accuracy and physical interpretability.
The two fundamental conflicting requirements of fuzzy modeling, namely accuracy and transparency (interpretability) have resulted in a plethora of design methodologies and ensuing architectures of fuzzy models.
