What's SHAP contribution dependency plots from xgboost package in R? clf = xgboost(data.matrix(train2), label = train$MACROSOMIA,objective = "binary:logistic", nrounds = 20)
pred = predict(clf, data.matrix(test[, -12]))

xgb.plot.shap(data.matrix(test2), model = clf, plot_loess =TRUE,features= "GESTAC")


This is my code, and I get a SHAP feature contribution to prediction dependencies on feature value. I just have a hard time understanding it. I wonder what is it? How do I interpret it? 

Here's a link! Could you help me understand what does this plot mean?

xgb.plot.shap()
On the y axis. That represents the log odd ratios. The formula is log(p/1-p)

P/1-P  P represents probability of success. 1- P represents probability of failure.

When log odd ratio is 0. It means odd is 1. Probability of success is the same as the probability of failure. 

When log odd ratio is below 0, it means that the probability of sucess is less than probability of failure.  

When log odd ratio is above 0, it means that the probability of sucess is higher than probability of failure.