Hands-On Machine Learning for Algorithmic Trading

How to inspect the tree structure

The notebook also illustrates how to run cross-validation more manually to obtain custom tree attributes, such as the total number of nodes or leaf nodes associated with certain hyperparameter settings. The following function accesses the internal .tree_ attribute to retrieve information about the total node count, and how many of these nodes are leaf nodes:

def get_leaves_count(tree):    t = tree.tree_    n = t.node_count    leaves = len([i for i in range(t.node_count) if t.children_left[i]== -1])    return leaves

We can combine this information with the train and test scores to gain detailed knowledge about the model behavior throughout the cross-validation process, as follows:

train_scores, val_scores, leaves = {} ...