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?
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