A facet plot is a useful way to show interaction effects when you have more than two variables, such as age, gender, and income. You can create multiple plots of the same type for each level of ...

Key Points Interactions can be seen with an interaction plot. Only worry about two-way interactions when conducting your DOE.

Interaction effects are when the effect of one variable on an outcome depends on the value of another variable. For example, the effect of exercise on weight loss may depend on the diet.

We have already seen that varying two factors simultaneously provides an effective experimental design for exploring the main (average) effects and interactions of the factors 1. However, in ...

Interaction plots are useful to evaluate effects when the number of factors is small (line plots, Fig 1b). The x axis represents levels of one factor and lines correspond to levels of other ...

I got the SHAP interaction values, using TreeExplainer for a xgboost model, and able to plot them using summary_plot. shap_interaction_values = treeExplainer.shap_interaction_values(x1) ...