I have trained a random forest classifier using the sklearn Python package, and used it to classify a datapoint with a certain feature vector.
Let's assume that the random forest has only one tree, that this is a binary classification task, and the data point has been labeled as class '0', while I was expecting it to be '1'. How can I check which features were responsible for such classification? Is there a way to get the list of split-thresholds for each feature?
How can this be generalised to the multiclass case, with multiple trees?