A Data Robot prediction for a binary classifier is a certainty of classification score between 0 and 1. The certainty that the row denotes a positive case. A corresponding explanation is a triple (f,v,s) giving a feature, its value, and the strength of the impact.
I have heard it said that the strength is something like the rate of change of the certainty with the value. But, given two features f1 and f2, which have a big step impact and a small affine impact, f1 might have the smaller strength in that sense, but be the more important feature in determining the conclusion.
So, in this case - in explicit Data Robot terms - which has the highest strength? and why?
What does the strength given tell you about what is the best thing to try changing to increase the probability.