Interpreting decision tree results after target encoding I am not sure how to interpret the results of my decision tree after I had used target encoding, could someone clarify? The example below doesn't need target encoding just for explanation of my confusion here.
For instance I am trying to classify if a fruit is rotten or not given its age and fruit type. I use target encoding for the fruit column:

I then get the following decision tree with default sklearn decision tree classifier parameters:

I believe after encoding I have lost information about fruit type and I can only say that if fruit_target &lt;= 0.841 then the fruit is rotten if smaller, else not rotten. But then how do i interpret 0.841; what does it mean?
I believe after encoding I have lost information about fruit type and I can only say that if fruit_target &lt;= 0.841 then the fruit is rotten if smaller, else not rotten. But then how do i interpret 0.841; what does it mean?

Recall what the target encoding actually is in this example: it is the share of rotten fruits per fruit type, e.g. $75 \%$ of data points with fruit == pear are estimated to be rotten (I say &quot;estimated&quot; because it depends on the type of target encoding whether this an exact number or an estimate).
Accordingly, you can infer from the decision tree that a data point will be classified as rotten iff its fruit type has more than $0.841 = 84.1\%$ rotten data points in the training set.