Hi Pranav, The reason you're getting that output is that your first column has a single value (1), and that becomes your "first" class, hence your first value in the rows you're interpreting.
To understand it better, you can try to check this code: >>> from sklearn.preprocessing import MultiLabelBinarizer >>> from sklearn.tree import DecisionTreeClassifier >>> >>> X = [[2, 51], [3, 20], [5, 30], [7, 1], [20, 46], [25, 25], [45, 70]] >>> Y = [[2,3],[1,2,3],[1,2,3],[1,2],[1,2],[1],[1]] >>> >>> y = MultiLabelBinarizer().fit_transform(Y) + 40 >>> y[0, 1] = 0 >>> >>> clf = DecisionTreeClassifier().fit(X, y) >>> print(clf.tree_.value) [[[1. 6. 0.] [1. 2. 4.] [4. 3. 0.]] [[1. 2. 0.] [1. 0. 2.] [0. 3. 0.]] [[0. 2. 0.] [0. 0. 2.] [0. 2. 0.]] [[1. 0. 0.] [1. 0. 0.] [0. 1. 0.]] [[0. 4. 0.] [0. 2. 2.] [4. 0. 0.]] [[0. 2. 0.] [0. 0. 2.] [2. 0. 0.]] [[0. 2. 0.] [0. 2. 0.] [2. 0. 0.]]] On Mon, 8 Oct 2018 at 20:53 Pranav Ashok <pranavas...@gmail.com> wrote: > I have a multi-class multi-label decision tree learnt using > DecisionTreeClassifier class. The input looks like follows: > > X = [[2, 51], [3, 20], [5, 30], [7, 1], [20, 46], [25, 25], [45, 70]] > Y = [[1,2,3],[1,2,3],[1,2,3],[1,2],[1,2],[1],[1]] > > I have used MultiLabelBinarizer to convert Y into > > [[1 1 1] > [1 1 1] > [1 1 1] > [1 1 0] > [1 1 0] > [1 0 0] > [1 0 0]] > > > After training, the _tree.values looks like follows: > > array([[[7., 0.], > [2., 5.], > [4., 3.]], > > [[3., 0.], > [0., 3.], > [0., 3.]], > > [[4., 0.], > [2., 2.], > [4., 0.]], > > [[2., 0.], > [0., 2.], > [2., 0.]], > > [[2., 0.], > [2., 0.], > [2., 0.]]]) > > I had the impression that the value array contains for each node, a list of > lists [[n_1, y_1], [n_2, y_2], [n_3, y_3]] > such that n_i are the number of samples disagreeing with class i and y_i are > the number of samples agreeing with > class i. But after seeing this output, it does not make sense. > > For example, the root node has the value [[7,0],[2,5],[4,3]]. According to my > interpretation, this would mean > 7 samples disagree with class 1; 2 disagree with class 2 and 5 agree with > class 2; 4 disagree with class 3 and 3 agree with class 3. > > which, according to the input dataset is wrong. > > Could someone please help me understand the semantics of _tree.value for > multi-label DTs? > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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