MachineLearning/tree at main · ttenneb/MachineLearning · GitHub

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digraph Tree {
node [shape=box, fontname="helvetica"] ;
edge [fontname="helvetica"] ;
0 [label="X[0] <= 3.422\nsquared_error = 311.476\nsamples = 10000\nvalue = -28.401"] ;
1 [label="X[0] <= 2.533\nsquared_error = 78.5\nsamples = 6691\nvalue = -18.551"] ;
0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
2 [label="X[0] <= 1.947\nsquared_error = 28.536\nsamples = 3277\nvalue = -11.219"] ;
1 -> 2 ;
3 [label="X[0] <= 1.521\nsquared_error = 15.596\nsamples = 1495\nvalue = -7.041"] ;
2 -> 3 ;
4 [label="X[1] <= -1.675\nsquared_error = 10.359\nsamples = 688\nvalue = -4.485"] ;
3 -> 4 ;
5 [label="squared_error = 6.675\nsamples = 427\nvalue = -6.135"] ;
4 -> 5 ;
6 [label="squared_error = 4.642\nsamples = 261\nvalue = -1.786"] ;
4 -> 6 ;
7 [label="X[1] <= -2.149\nsquared_error = 9.746\nsamples = 807\nvalue = -9.22"] ;
3 -> 7 ;
8 [label="squared_error = 5.728\nsamples = 373\nvalue = -11.579"] ;
7 -> 8 ;
9 [label="squared_error = 4.305\nsamples = 434\nvalue = -7.192"] ;
7 -> 9 ;
10 [label="X[1] <= -1.972\nsquared_error = 12.454\nsamples = 1782\nvalue = -14.725"] ;
2 -> 10 ;
11 [label="X[0] <= 2.286\nsquared_error = 8.217\nsamples = 896\nvalue = -16.917"] ;
10 -> 11 ;
12 [label="squared_error = 4.322\nsamples = 477\nvalue = -15.119"] ;
11 -> 12 ;
13 [label="squared_error = 4.778\nsamples = 419\nvalue = -18.964"] ;
11 -> 13 ;
14 [label="X[0] <= 2.27\nsquared_error = 6.967\nsamples = 886\nvalue = -12.508"] ;
10 -> 14 ;
15 [label="squared_error = 3.969\nsamples = 437\nvalue = -10.843"] ;
14 -> 15 ;
16 [label="squared_error = 4.558\nsamples = 449\nvalue = -14.129"] ;
14 -> 16 ;
17 [label="X[0] <= 2.997\nsquared_error = 25.345\nsamples = 3414\nvalue = -25.588"] ;
1 -> 17 ;
18 [label="X[1] <= -2.217\nsquared_error = 12.511\nsamples = 1773\nvalue = -22.159"] ;
17 -> 18 ;
19 [label="X[0] <= 2.794\nsquared_error = 7.799\nsamples = 775\nvalue = -24.643"] ;
18 -> 19 ;
20 [label="squared_error = 5.674\nsamples = 404\nvalue = -22.994"] ;
19 -> 20 ;
21 [label="squared_error = 3.92\nsamples = 371\nvalue = -26.44"] ;
19 -> 21 ;
22 [label="X[0] <= 2.771\nsquared_error = 7.656\nsamples = 998\nvalue = -20.23"] ;
18 -> 22 ;
23 [label="squared_error = 4.618\nsamples = 456\nvalue = -18.283"] ;
22 -> 23 ;
24 [label="squared_error = 4.34\nsamples = 542\nvalue = -21.868"] ;
22 -> 24 ;
25 [label="X[1] <= -1.821\nsquared_error = 12.782\nsamples = 1641\nvalue = -29.293"] ;
17 -> 25 ;
26 [label="X[0] <= 3.22\nsquared_error = 8.717\nsamples = 942\nvalue = -31.145"] ;
25 -> 26 ;
27 [label="squared_error = 5.082\nsamples = 494\nvalue = -29.359"] ;
26 -> 27 ;
28 [label="squared_error = 5.327\nsamples = 448\nvalue = -33.115"] ;
26 -> 28 ;
29 [label="X[0] <= 3.209\nsquared_error = 7.409\nsamples = 699\nvalue = -26.797"] ;
25 -> 29 ;
30 [label="squared_error = 3.29\nsamples = 364\nvalue = -24.873"] ;
29 -> 30 ;
31 [label="squared_error = 3.49\nsamples = 335\nvalue = -28.888"] ;
29 -> 31 ;
32 [label="X[0] <= 4.327\nsquared_error = 189.626\nsamples = 3309\nvalue = -48.32"] ;
0 -> 32 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
33 [label="X[0] <= 3.848\nsquared_error = 39.996\nsamples = 2427\nvalue = -41.714"] ;
32 -> 33 ;
34 [label="X[1] <= -1.912\nsquared_error = 14.251\nsamples = 1359\nvalue = -37.378"] ;
33 -> 34 ;
35 [label="X[0] <= 3.61\nsquared_error = 10.249\nsamples = 701\nvalue = -39.449"] ;
34 -> 35 ;
36 [label="squared_error = 5.057\nsamples = 355\nvalue = -37.327"] ;
35 -> 36 ;
37 [label="squared_error = 6.214\nsamples = 346\nvalue = -41.627"] ;
35 -> 37 ;
38 [label="X[0] <= 3.621\nsquared_error = 9.079\nsamples = 658\nvalue = -35.172"] ;
34 -> 38 ;
39 [label="squared_error = 3.659\nsamples = 311\nvalue = -32.886"] ;
38 -> 39 ;
40 [label="squared_error = 5.053\nsamples = 347\nvalue = -37.221"] ;
38 -> 40 ;
41 [label="X[0] <= 4.108\nsquared_error = 18.405\nsamples = 1068\nvalue = -47.231"] ;
33 -> 41 ;
42 [label="X[1] <= -2.259\nsquared_error = 11.49\nsamples = 636\nvalue = -45.001"] ;
41 -> 42 ;
43 [label="squared_error = 7.293\nsamples = 254\nvalue = -47.761"] ;
42 -> 43 ;
44 [label="squared_error = 5.847\nsamples = 382\nvalue = -43.166"] ;
42 -> 44 ;
45 [label="X[1] <= -1.966\nsquared_error = 10.491\nsamples = 432\nvalue = -50.513"] ;
41 -> 45 ;
46 [label="squared_error = 6.044\nsamples = 210\nvalue = -52.819"] ;
45 -> 46 ;
47 [label="squared_error = 4.909\nsamples = 222\nvalue = -48.332"] ;
45 -> 47 ;
48 [label="X[0] <= 5.028\nsquared_error = 150.884\nsamples = 882\nvalue = -66.497"] ;
32 -> 48 ;
49 [label="X[0] <= 4.626\nsquared_error = 34.529\nsamples = 675\nvalue = -61.18"] ;
48 -> 49 ;
50 [label="X[1] <= -1.791\nsquared_error = 11.634\nsamples = 374\nvalue = -57.074"] ;
49 -> 50 ;
51 [label="squared_error = 8.367\nsamples = 215\nvalue = -58.854"] ;
50 -> 51 ;
52 [label="squared_error = 5.974\nsamples = 159\nvalue = -54.667"] ;
50 -> 52 ;
53 [label="X[0] <= 4.802\nsquared_error = 15.995\nsamples = 301\nvalue = -66.282"] ;
49 -> 53 ;
54 [label="squared_error = 9.13\nsamples = 161\nvalue = -63.842"] ;
53 -> 54 ;
55 [label="squared_error = 9.167\nsamples = 140\nvalue = -69.088"] ;
53 -> 55 ;
56 [label="X[0] <= 5.753\nsquared_error = 137.558\nsamples = 207\nvalue = -83.834"] ;
48 -> 56 ;
57 [label="X[0] <= 5.275\nsquared_error = 39.4\nsamples = 179\nvalue = -80.17"] ;
56 -> 57 ;
58 [label="squared_error = 12.477\nsamples = 99\nvalue = -75.609"] ;
57 -> 58 ;
59 [label="squared_error = 15.123\nsamples = 80\nvalue = -85.814"] ;
57 -> 59 ;
60 [label="X[0] <= 6.272\nsquared_error = 130.609\nsamples = 28\nvalue = -107.257"] ;
56 -> 60 ;
61 [label="squared_error = 30.637\nsamples = 22\nvalue = -102.483"] ;
60 -> 61 ;
62 [label="squared_error = 107.228\nsamples = 6\nvalue = -124.761"] ;
60 -> 62 ;
}