<jupyter_start><jupyter_text><jupyter_code>from sklearn import tree
X = [[0, 0], [1, 1]] #feture variable 
Y = [0, 1] #target 
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)<jupyter_output><empty_output><jupyter_text>DecisionTreeClassifier takes as input two arrays: an array X, sparse or dense, of size [n_samples, n_features] holding the training samples, and an array Y of integer values, size [n_samples], holding the class labels for the training samples:<jupyter_code>clf.predict([[0., 0.]])<jupyter_output><empty_output><jupyter_text>After being fitted, the model can then be used to predict the class of samples:<jupyter_code>clf.predict_proba([[2., 2.]])<jupyter_output><empty_output>
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