I have the feature contribution info from LIME like as shown below (for predicted class = 1)
Predicted class probability 0 = 0.22 (blue) Predicted class probability 1 = 0.78 (orange) Div=sales= 0.09 (blue) Quantity=50000= 0.06 (blue) Supplier=XXX= 0.05 (blue) SUBREGION= 0.04 (blue) Product Classification = 0.02 (orange)
The below is the another example
value refers to feature importance returned by Lime. negative values indicate blue color items (for class 0) and positive values indicate orange color items (for class 1)
class 1 prob = 57% class 0 prob = 43%
My questions are as follows
a) Why is the negative feature influence (sum of blue values) is higher than positive value features (sum of orange values)? especially for class = 1? In that case, shouldn't the output be class=0? Is it making use of Intercept? How should I interpret this? I have shown only top 5 features but rest of the features also have negative values (decreasing order).
b) If I am presenting this to the business, how should I explain to the business in a scenario like this where for a positive class, negative feature influence is higher (and I guess due to intercept value, it became as positive class)?