Furthermore, there is a clear trade-off between model complexity, algorithmic feasibility of models, and interpretability.
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.
The choice of the model must be defensible using statistical and substantive considerations such as model complexity and interpretability.
This partitioning of the Xdata results in improved model transparency and interpretability [75].
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.
The modeling accuracy, model interpretability, and procedures to perform parameter with relative importance are presented.
Often a modeller will choose a trade-off between the performance of the model and the interpretability of the prediction according to the purpose of making a prediction.
Although the accuracy of SAR and QSAR models has for a long time been the primary concern and used as the main performance measure [2], there are other important facets amongst which are: The transparency of the model and the interpretability of the predictions they provide [3 5].
Regression models had good predictive power (R from 0.89 to 0.98 across models) and physical interpretability, and outputs corresponded well with literature values [ 75].
R has several appealing properties, of which the most important are: i) R∈ [0,1]: it lies between 0 (representing no predictive ability) and 1 (perfect predictive ability); ii) monotonicity: it increases with the size of the covariate effect, ∥ β∥, in the model; and iii) interpretability as the percentage of variability in the outcome that is explained by the covariates [ 1].
