fisherのアヤメデータ
code:p.py
from sklearn.datasets import load_iris
X, y = load_iris(return_X_y=True)
https://drive.google.com/file/d/1VIastC3yf8ykHs66K6_sgDD4nU_xWK3Z/view?usp=drive_link
code:data.csv
sepal length (cm),sepal width (cm),petal length (cm),petal width (cm)
0,5.1,3.5,1.4,0.2
1,4.9,3.0,1.4,0.2
2,4.7,3.2,1.3,0.2
3,4.6,3.1,1.5,0.2
4,5.0,3.6,1.4,0.2
5,5.4,3.9,1.7,0.4
6,4.6,3.4,1.4,0.3
7,5.0,3.4,1.5,0.2
8,4.4,2.9,1.4,0.2
9,4.9,3.1,1.5,0.1
10,5.4,3.7,1.5,0.2
11,4.8,3.4,1.6,0.2
12,4.8,3.0,1.4,0.1
13,4.3,3.0,1.1,0.1
14,5.8,4.0,1.2,0.2
15,5.7,4.4,1.5,0.4
16,5.4,3.9,1.3,0.4
17,5.1,3.5,1.4,0.3
18,5.7,3.8,1.7,0.3
19,5.1,3.8,1.5,0.3
20,5.4,3.4,1.7,0.2
21,5.1,3.7,1.5,0.4
22,4.6,3.6,1.0,0.2
23,5.1,3.3,1.7,0.5
24,4.8,3.4,1.9,0.2
25,5.0,3.0,1.6,0.2
26,5.0,3.4,1.6,0.4
27,5.2,3.5,1.5,0.2
28,5.2,3.4,1.4,0.2
29,4.7,3.2,1.6,0.2
30,4.8,3.1,1.6,0.2
31,5.4,3.4,1.5,0.4
32,5.2,4.1,1.5,0.1
33,5.5,4.2,1.4,0.2
34,4.9,3.1,1.5,0.2
35,5.0,3.2,1.2,0.2
36,5.5,3.5,1.3,0.2
37,4.9,3.6,1.4,0.1
38,4.4,3.0,1.3,0.2
39,5.1,3.4,1.5,0.2
40,5.0,3.5,1.3,0.3
41,4.5,2.3,1.3,0.3
42,4.4,3.2,1.3,0.2
43,5.0,3.5,1.6,0.6
44,5.1,3.8,1.9,0.4
45,4.8,3.0,1.4,0.3
46,5.1,3.8,1.6,0.2
47,4.6,3.2,1.4,0.2
48,5.3,3.7,1.5,0.2
49,5.0,3.3,1.4,0.2
50,7.0,3.2,4.7,1.4
51,6.4,3.2,4.5,1.5
52,6.9,3.1,4.9,1.5
53,5.5,2.3,4.0,1.3
54,6.5,2.8,4.6,1.5
55,5.7,2.8,4.5,1.3
56,6.3,3.3,4.7,1.6
57,4.9,2.4,3.3,1.0
58,6.6,2.9,4.6,1.3
59,5.2,2.7,3.9,1.4
60,5.0,2.0,3.5,1.0
61,5.9,3.0,4.2,1.5
62,6.0,2.2,4.0,1.0
63,6.1,2.9,4.7,1.4
64,5.6,2.9,3.6,1.3
65,6.7,3.1,4.4,1.4
66,5.6,3.0,4.5,1.5
67,5.8,2.7,4.1,1.0
68,6.2,2.2,4.5,1.5
69,5.6,2.5,3.9,1.1
70,5.9,3.2,4.8,1.8
71,6.1,2.8,4.0,1.3
72,6.3,2.5,4.9,1.5
73,6.1,2.8,4.7,1.2
74,6.4,2.9,4.3,1.3
75,6.6,3.0,4.4,1.4
76,6.8,2.8,4.8,1.4
77,6.7,3.0,5.0,1.7
78,6.0,2.9,4.5,1.5
79,5.7,2.6,3.5,1.0
80,5.5,2.4,3.8,1.1
81,5.5,2.4,3.7,1.0
82,5.8,2.7,3.9,1.2
83,6.0,2.7,5.1,1.6
84,5.4,3.0,4.5,1.5
85,6.0,3.4,4.5,1.6
86,6.7,3.1,4.7,1.5
87,6.3,2.3,4.4,1.3
88,5.6,3.0,4.1,1.3
89,5.5,2.5,4.0,1.3
90,5.5,2.6,4.4,1.2
91,6.1,3.0,4.6,1.4
92,5.8,2.6,4.0,1.2
93,5.0,2.3,3.3,1.0
94,5.6,2.7,4.2,1.3
95,5.7,3.0,4.2,1.2
96,5.7,2.9,4.2,1.3
97,6.2,2.9,4.3,1.3
98,5.1,2.5,3.0,1.1
99,5.7,2.8,4.1,1.3
100,6.3,3.3,6.0,2.5
101,5.8,2.7,5.1,1.9
102,7.1,3.0,5.9,2.1
103,6.3,2.9,5.6,1.8
104,6.5,3.0,5.8,2.2
105,7.6,3.0,6.6,2.1
106,4.9,2.5,4.5,1.7
107,7.3,2.9,6.3,1.8
108,6.7,2.5,5.8,1.8
109,7.2,3.6,6.1,2.5
110,6.5,3.2,5.1,2.0
111,6.4,2.7,5.3,1.9
112,6.8,3.0,5.5,2.1
113,5.7,2.5,5.0,2.0
114,5.8,2.8,5.1,2.4
115,6.4,3.2,5.3,2.3
116,6.5,3.0,5.5,1.8
117,7.7,3.8,6.7,2.2
118,7.7,2.6,6.9,2.3
119,6.0,2.2,5.0,1.5
120,6.9,3.2,5.7,2.3
121,5.6,2.8,4.9,2.0
122,7.7,2.8,6.7,2.0
123,6.3,2.7,4.9,1.8
124,6.7,3.3,5.7,2.1
125,7.2,3.2,6.0,1.8
126,6.2,2.8,4.8,1.8
127,6.1,3.0,4.9,1.8
128,6.4,2.8,5.6,2.1
129,7.2,3.0,5.8,1.6
130,7.4,2.8,6.1,1.9
131,7.9,3.8,6.4,2.0
132,6.4,2.8,5.6,2.2
133,6.3,2.8,5.1,1.5
134,6.1,2.6,5.6,1.4
135,7.7,3.0,6.1,2.3
136,6.3,3.4,5.6,2.4
137,6.4,3.1,5.5,1.8
138,6.0,3.0,4.8,1.8
139,6.9,3.1,5.4,2.1
140,6.7,3.1,5.6,2.4
141,6.9,3.1,5.1,2.3
142,5.8,2.7,5.1,1.9
143,6.8,3.2,5.9,2.3
144,6.7,3.3,5.7,2.5
145,6.7,3.0,5.2,2.3
146,6.3,2.5,5.0,1.9
147,6.5,3.0,5.2,2.0
148,6.2,3.4,5.4,2.3
149,5.9,3.0,5.1,1.8
code:targer.csv
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