Interpreting decision tree results after target encoding
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.