Hi Adrin,
Thanks for the clarification. Is there a right way of letting
DecisionTreeClassifier know that the first column can take both 0 or 1, but
in the current dataset we are only using 0?
For example, we can let MultiLabelBinarizer know that we have three classes
by instantiating it like this
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 MultiLabe
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
Good to know!
El lun., 8 oct. 2018 9:08, Joel Nothman escribió:
> Just a note that multiple layers of stacking can be achieved with
> StackingClassifier using nesting.
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Just a note that multiple layers of stacking can be achieved with
StackingClassifier using nesting.
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