[scikit-learn] Need Corresponding indices array of values in each split of a DesicisionTreeClassifier

[Jeff Blackburne]

Nixon,

If you are using version 0.18 or later, you can reconstruct the information
you need using the `decision_path` method:

http://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html

-Jeff


On Tue, Feb 7, 2017 at 3:21 PM, Joel Nothman <joel.nothman at gmail.com> wrote:

> I don't think putting that array of indices in a visualisation is a great
> idea!
>
> If you use my_tree.apply(X) you will be able to determine which leaf each
> instance in X lands up at, and potentially trace up the tree from there.
>
> On 8 February 2017 at 01:26, Nixon Raj <nixnmtm at gmail.com> wrote:
>
>>
>> For Example, In the below decision tree dot file, I have 223 samples
>> which splits into [174, 49] in the first split and [110, 1] in the 2nd split
>>
>> I would like to get the array of indices for the values of each split
>> like
>>
>> *[174, 49] and their corresponding indices (idx)  like [[0, 1 ,5,
>> 7,....,200,221], [3, 4, 6, ....., 199,222,223]]*
>>
>> *[110, 1] and their corresponding indices (idx) like [[0,5,....200,221],
>> [7]]*
>>
>> Please help me
>>
>> node [shape=box] ;
>> 0 [label="X[0] <= 13.9191\nentropy = 0.7597\nsamples = 223\nvalue = [174,
>> 49]"] ;
>> 1 [label="X[1] <= 3.1973\nentropy = 0.0741\nsamples = 111\nvalue = [110,
>> 1]"] ;
>> 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
>> 2 [label="entropy = 0.0\nsamples = 109\nvalue = [109, 0]"] ;
>> 1 -> 2 ;
>> 3 [label="entropy = 1.0\nsamples = 2\nvalue = [1, 1]"] ;
>> 1 -> 3 ;
>> 4 [label="X[1] <= 3.1266\nentropy = 0.9852\nsamples = 112\nvalue = [64,
>> 48]"] ;
>> 0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
>> 5 [label="X[2] <= -0.4882\nentropy = 0.7919\nsamples = 63\nvalue = [48,
>> 15]"] ;
>> 4 -> 5 ;
>> 6 [label="entropy = 0.684\nsamples = 11\nvalue = [2, 9]"] ;
>> 5 -> 6 ;
>> 7 [label="X[2] <= 0.5422\nentropy = 0.5159\nsamples = 52\nvalue = [46,
>> 6]"] ;
>> 5 -> 7 ;
>> 8 [label="entropy = 0.0\nsamples = 18\nvalue = [18, 0]"] ;
>> 7 -> 8 ;
>> 9 [label="X[2] <= 0.6497\nentropy = 0.6723\nsamples = 34\nvalue = [28,
>> 6]"] ;
>> 7 -> 9 ;
>> 10 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]"] ;
>> 9 -> 10 ;
>> 11 [label="X[2] <= 1.887\nentropy = 0.6136\nsamples = 33\nvalue = [28,
>> 5]"] ;
>> 9 -> 11 ;
>> 12 [label="entropy = 0.0\nsamples = 12\nvalue = [12, 0]"] ;
>> 11 -> 12 ;
>> 13 [label="X[2] <= 2.6691\nentropy = 0.7919\nsamples = 21\nvalue = [16,
>> 5]"] ;
>> 11 -> 13 ;
>> 14 [label="entropy = 0.8113\nsamples = 4\nvalue = [1, 3]"] ;
>> 13 -> 14 ;
>> 15 [label="entropy = 0.5226\nsamples = 17\nvalue = [15, 2]"] ;
>> 13 -> 15 ;
>> 16 [label="X[0] <= 17.3284\nentropy = 0.9113\nsamples = 49\nvalue = [16,
>> 33]"] ;
>> 4 -> 16 ;
>> 17 [label="entropy = 0.9183\nsamples = 6\nvalue = [4, 2]"] ;
>> 16 -> 17 ;
>> 18 [label="X[2] <= 19.7048\nentropy = 0.8542\nsamples = 43\nvalue = [12,
>> 31]"] ;
>> 16 -> 18 ;
>> 19 [label="X[2] <= 5.8511\nentropy = 0.8296\nsamples = 42\nvalue = [11,
>> 31]"] ;
>> 18 -> 19 ;
>> 20 [label="X[0] <= 31.8916\nentropy = 0.878\nsamples = 37\nvalue = [11,
>> 26]"] ;
>> 19 -> 20 ;
>> 21 [label="X[1] <= 3.3612\nentropy = 0.6666\nsamples = 23\nvalue = [4,
>> 19]"] ;
>> 20 -> 21 ;
>> 22 [label="entropy = 0.8905\nsamples = 13\nvalue = [4, 9]"] ;
>> 21 -> 22 ;
>> 23 [label="entropy = 0.0\nsamples = 10\nvalue = [0, 10]"] ;
>> 21 -> 23 ;
>> 24 [label="entropy = 1.0\nsamples = 14\nvalue = [7, 7]"] ;
>> 20 -> 24 ;
>> 25 [label="entropy = 0.0\nsamples = 5\nvalue = [0, 5]"] ;
>> 19 -> 25 ;
>> 26 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]"] ;
>> 18 -> 26 ;
>> }
>>
>>
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