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

[Joel Nothman]

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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