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 1-unit 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 – calculating how much worse the model becomes if we remove (scramble) the information in the feature

Method 5: Shapley Additive Explanations (SHAP) – these measure the influence of a feature by comparing model predictions with and without the feature (https://www.actuaries.digital/2019/06/18/analytics-snippet-feature-importance-and-the-shap-approach-to-machine-learning-models/)

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)