kaggle tutorial: https://www.kaggle.com/learn/machine-learning-explainability

Permutation importance

https://www.kaggle.com/dansbecker/permutation-importance

关键点：Instead we will ask the following question: If I randomly shuffle a single column of the validation data, leaving the target and all other columns in place, how would that affect the accuracy of predictions in that now-shuffled data?

partial dependence plots (PDP)

https://www.kaggle.com/dansbecker/partial-plots

To see how partial plots separate out the effect of each feature, we start by considering a single row of data. For example, that row of data might represent a team that had the ball 50% of the time, made 100 passes, took 10 shots and scored 1 goal.

We will use the fitted model to predict our outcome (probability their player won “man of the match”). But we repeatedly alter the value for one variable to make a series of predictions. We could predict the outcome if the team had the ball only 40% of the time. We then predict with them having the ball 50% of the time. Then predict again for 60%. And so on. We trace out predicted outcomes (on the vertical axis) as we move from small values of ball possession to large values (on the horizontal axis).

SHAP （SHapley Additive exPlanations）

https://www.kaggle.com/dansbecker/shap-values

理论： https://towardsdatascience.com/one-feature-attribution-method-to-supposedly-rule-them-all-shapley-values-f3e04534983d