Python – Using sklearn, how to find depth of a decision tree

decision-treepythonscikit-learn

I am training a decision tree with sklearn. When I use:

dt_clf = tree.DecisionTreeClassifier()

the max_depth parameter defaults to None. According to the documentation, if max_depth is None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

After fitting my model, how do I find out what max_depth actually is? The get_params() function doesn't help. After fitting, get_params() it still says None.

How can I get the actual number for max_depth?

Docs: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html

Best Answer

Access the max_depth for the underlying Tree object:

from sklearn import tree
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)
print(clf.tree_.max_depth)
>>> 1

You may get more accessible attributes from the underlying tree object using:

help(clf.tree_)

These include max_depth, node_count, and other lower-level parameters.

Related Solutions

Python – Factor Loadings using sklearn

Multiply each component by the square root of its corresponding eigenvalue:

pca.components_.T * np.sqrt(pca.explained_variance_)

This should produce your loading matrix.

Python – How to access tree depth in Python’s scikit-learn

Each instance of RandomForestClassifier has an estimators_ attribute, which is a list of DecisionTreeClassifier instances. The documentation shows that an instance of DecisionTreeClassifier has a tree_ attribute, which is an instance of the (undocumented, I believe) Tree class. Some exploration in the interpreter shows that each Tree instance has a max_depth parameter which appears to be what you're looking for -- again, it's undocumented.

In any case, if forest is your instance of RandomForestClassifier, then:

>>> [estimator.tree_.max_depth for estimator in forest.estimators_]
[9, 10, 9, 11, 9, 9, 11, 7, 13, 10]

should do the trick.

Each estimator also has a get_depth() method than can be used to retrieve the same value with briefer syntax:

>>> [estimator.get_depth() for estimator in forest.estimators_]
[9, 10, 9, 11, 9, 9, 11, 7, 13, 10]

To avoid mixup, it should be noted that there is an attribute of each estimator (and not each estimator's tree_) called max depth which returns the setting of the parameter rather than the depth of the actual tree. How estimator.get_depth(), estimator.tree_.max_depth, and estimator.max_depth relate to each other is clarified in the example below:

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=3, random_state=4, max_depth=6)
iris = load_iris()
clf.fit(iris['data'], iris['target'])
[(est.get_depth(), est.tree_.max_depth, est.max_depth) for est in clf.estimators_]

Out:

[(6, 6, 6), (3, 3, 6), (4, 4, 6)]

Setting max depth to the default value None would allow the first tree to expand to depth 7 and the output would be:

[(7, 7, None), (3, 3, None), (4, 4, None)]

Best Answer

Related Solutions

Python – Factor Loadings using sklearn

Python – How to access tree depth in Python’s scikit-learn

Related Topic