Sentence examples for model interpretability and from inspiring English sources

The modeling accuracy, model interpretability, and procedures to perform parameter with relative importance are presented.

To improve model interpretability and generalization, sparsity is imposed over these hidden relations.

The incorporation of study-specific dispersion principles may corroborate overall LUR model interpretability and could be applied similarly to preexisting LUR models that lack strong source-concentration relationships.

In general, centering enhances model interpretability and improves numerical stability by increasing the likelihood that the optimization algorithm converges on the correct solution.

Furthermore, any preference for model interpretability and the selection of a GLM based model may be somewhat self fulfilling where GLMs tend to perform best (among other GLMs, but not globally best) with a smaller number of features when compared to ANN or SVM models.

Feature selection is a crucial step to reduce computation time and storage, improve model interpretability, understanding, performance, and remove irrelevant features (noisy data) to avoid over fitting [87].

We also point out that an ideal design of fuzzy rule-based models for microarray gene expression data analysis includes two important tasks: designing low-complexity fuzzy models, and finding trade-off points between classification accuracy and model interpretability.

We address the challenge of learning complex relation patterns from data objects exhibiting heterogeneous properties, and develop an effective multi-view multimodal similarity learning model with much improved learning performance and model interpretability.

Extensive simulation studies and an fMRI (functional Magnetic Resonance Imaging) study show that FAC is extremely competitive in comparison to other potential approaches in terms of both classification accuracy and model interpretability.

This can greatly diminish model interpretability by both users and software tools.

In this section, we describe how to apply multi-objective evolutionary algorithms (MOEA) to extract fuzzy rule sets considering the balance between model accuracy and model interpretability.