This is a model modelStudio for R  based on a model  SimThyr  of the relationship between TRH and FT4. It shows the features and their influence on the contribution to changes in the thyroid hormones  shows which variables are most important for a specific instance at this page FT4
Break down: shows which variables are most important for a specific instance at this page FT4
Intercept: The intercept in a multiple regression model is the mean for the response when all of the explanatory variables take on the value 0.
Prediction: A feature is “important” if permuting its values increases the model error relative to the other features, because the model relied on the feature for the prediction. A feature is “unimportant” if permuting its values keeps the model error relatively unchanged, because the model ignored the feature for the prediction.
Response: Regression Coefficients:
Typically the coefficient of a variable is interpreted as the change in the response based on a 1unit change in the corresponding explanatory variable keeping all other variables held constant. In some problems, keeping all other variables held fixed is impossible
The feature importance plot  overview
Method 4: Drop Out Loss â€“ calculating how much worse the model becomes if we remove (scramble) the information in the feature
Method 5: Shapley Additive Explanations (SHAP) â€“ these measure the influence of a feature by comparing model predictions with and without the feature (https://www.actuaries.digital/2019/06/18/analyticssnippetfeatureimportanceandtheshapapproachtomachinelearningmodels/)
The concept of Shapley values is based on the idea that the feature values of an individual observation work together to cause a change in the model’s prediction with respect to the model’s expected output, and it divides this total change in prediction among the features in a way that is “fair” to their contributions across all possible subsets of features. (https://bradleyboehmke.github.io/HOML/iml.html)
