# How to interpret Shapley value plot for a model?

I was trying to use Shapley value approach for understanding the model predictions. I am trying this on a Xgboost model. My plot looks like as below

Can someone help me interpret this? Or confirm my understanding is correct?

My interpretation

1) High values of Feature 5 (indicated by rose/purple combination) - leads to prediction 1

2) Low values of Feature 5 (indicated by blue) - leads to prediction 0

3) Step 1 and 2 applies for Feature 1 as well

4) Low values of Feature 6 leads to prediction 1 and high values of Feature 6 leads to Prediction 0

5) Low values of Feature 8 leads to prediction 1 and high values of Feature 8 leads to prediction 1 as well. If it's too the extreme of x-axis (meaning from x(1,2) or x(2,3) - it means the impact of low values (in this case) of this feature, has a huge impact on the prediction 1. Am I right?

6) Why don't I see all my 45 features in the plot irrespective of the importance/influence. Shouldn't I be seeing no color when they have no importance. Why is that I only see around 12-14 features?

7) What role does Feature 43,Feature 55, Feature 14 play in prediction output?

8) Why is the SHAP value range from -2,2?

Can someone help me with this?

• Hi, thanks for the response. Upvoted. One quick question. For point 8, what do you mean by output magnitude range of the model? In my case, the output label is 0 and 1. So the range of output label should be from 0 - 1? But I see -2 to + 2? I know am making a blunder. would be helpful if you could help me wit this? Dec 23 '19 at 11:05