[scikit-learn] Understanding sklearn.tree._tree.value object

[Pranav Ashok]

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: MultiLabelBinarizer([1,2,3]).

I tried class_weight=[{0: 1, 1: 1}, {0: 1, 1: 1}, {0: 1, 1: 1}] but that
doesn't work.

Thanks,
Pranav

On Mon, Oct 8, 2018 at 2:32 PM Adrin <adrin.jalali at gmail.com> wrote:

> 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 <pranavashok at 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?
>>
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