Decision Path For A Random Forest Classifier

Here is my code to run it in your environment, I am using the RandomForestClassifier and I am trying to figure out the decision_path for a selected sample in the RandomForestClassi

Solution 1:

RandomForestClassifier.decision_path method returns a tuple of (indicator, n_nodes_ptr). see the documentation : here

So your variable node_indicator is a tuple and not what you think it is. A tuple object has no attribute 'indices' that's why you get the error when you do :

node_index = node_indicator.indices[node_indicator.indptr[sample_id]:
                                    node_indicator.indptr[sample_id + 1]]

try :

(node_indicator, _) = rf.decision_path(X_train)

---

You can also plot the decision tree of each tree of the forest for a single sample id :

X_train = X_train.values

sample_id = 0for j, tree in enumerate(rf.estimators_):

    n_nodes = tree.tree_.node_count
    children_left = tree.tree_.children_left
    children_right = tree.tree_.children_right
    feature = tree.tree_.feature
    threshold = tree.tree_.threshold

    print("Decision path for DecisionTree {0}".format(j))
    node_indicator = tree.decision_path(X_train)
    leave_id = tree.apply(X_train)
    node_index = node_indicator.indices[node_indicator.indptr[sample_id]:
                                        node_indicator.indptr[sample_id + 1]]



    print('Rules used to predict sample %s: ' % sample_id)
    for node_id in node_index:
        if leave_id[sample_id] != node_id:
            continueif (X_train[sample_id, feature[node_id]] <= threshold[node_id]):
            threshold_sign = "<="else:
            threshold_sign = ">"print("decision id node %s : (X_train[%s, %s] (= %s) %s %s)"
              % (node_id,
                 sample_id,
                 feature[node_id],
                 X_train[sample_id, feature[node_id]],
                 threshold_sign,
                 threshold[node_id]))

Note that in your case, you have 50 estimators so it might be a bit boring to read.