Using Gini index, how to calculate the probability of correctly classifying a new randomly selected case to the highest probability class? I have the following binary Decision Tree:

Can you please explain how can I report this tree to a person who only understands probabilities?

If ca=1 and cp_4.0=1, what’s the probability of Yes HD?
While you can calculate the underlying class probabilities from the Gini index (for binary classification), it'll be more straightforward to calculate it from the &quot;value&quot; line in each box. This line simply represents the number of samples that belong in each node, split by the target variable, so you can use this count to calculate a probability.
In the bottom-most node, for example, you have value = [4, 55], indicating that among 59 samples which belong in that node (samples that have ca &gt; 0.5 and cp_4.0 &gt; 0.5), 4 are No HD and 55 are Yes HD. Therefore, the probability of Yes HD in that node is 55/59.
The Gini index is a measure of how &quot;pure&quot; a node is - as this number gets closer to 0, probability values will become more extreme (closer to 0 or 1), indicating that the decision tree is doing a better job of discriminating the target variable.