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When using sklearn.tree.DecisionTreeClassifier, the classifier has methods for predicting probability and class.
Is there a way to use the same tree for clustering: for a given input vector x, simply tell which leaf x belongs to?
745
Decision trees can also be used to perform clustering, with a few adjustments. On one hand, new split criteria must be discovered to construct the tree without the knowledge of samples la- bels. On the other hand, new algorithms must be applied to merge sub- clusters at leaf nodes into actual clusters.
Decision Tree Classifier: It's used to solve classification problems. For example, they are predicting if a person will have their loan approved. Decision Tree Regressor: It's used to solve regression problems. For example, prediction of how many people will die because of an opiate overdose.
datasets import load_iris >>> from sklearn. model_selection import cross_val_score >>> from sklearn. tree import DecisionTreeClassifier >>> clf = DecisionTreeClassifier(random_state=0) >>> iris = load_iris() >>> cross_val_score(clf, iris. data, iris.
I found the answer to my own question - leaving it here as reference for the next time someone looks for it:
import numpy as np
import sklearn.tree
clf = sklearn.tree.DecisionTreeClassifier()
clf.fit(X,y)
clf.tree_.apply(np.asfortranarray(X.astype(sklearn.tree._tree.DTYPE)))
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