Skip to content

Commit 93e33d6

Browse files
allenlu2008allenlu2008
committed
Signed-off-by: god xia <allenlu2008@163.com>
1 parent 4446440 commit 93e33d6

13 files changed

+4263
-0
lines changed

‎0005DecisionTreeRegressor/.ipynb_checkpoints/Untitled-checkpoint.ipynb‎

Lines changed: 126 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,126 @@
1+
{
2+
"cells": [
3+
{
4+
"cell_type": "code",
5+
"execution_count": 1,
6+
"metadata": {},
7+
"outputs": [],
8+
"source": [
9+
"from sklearn.datasets import load_boston\n",
10+
"boston = load_boston()"
11+
]
12+
},
13+
{
14+
"cell_type": "code",
15+
"execution_count": 4,
16+
"metadata": {},
17+
"outputs": [],
18+
"source": [
19+
"import sklearn.model_selection as skm"
20+
]
21+
},
22+
{
23+
"cell_type": "code",
24+
"execution_count": null,
25+
"metadata": {},
26+
"outputs": [],
27+
"source": []
28+
},
29+
{
30+
"cell_type": "code",
31+
"execution_count": 5,
32+
"metadata": {},
33+
"outputs": [],
34+
"source": [
35+
"import numpy as np\n",
36+
"X_train,X_test,y_train,y_test =skm.train_test_split(boston.data,\n",
37+
" boston.target,\n",
38+
" test_size=0.25,\n",
39+
" random_state=33)"
40+
]
41+
},
42+
{
43+
"cell_type": "code",
44+
"execution_count": 8,
45+
"metadata": {},
46+
"outputs": [
47+
{
48+
"data": {
49+
"text/plain": [
50+
"array([33.8, 20.3, 10.2, 22. , 21.2, 24.2, 29. , 22.7, 21.8, 34.9, 25.2,\n",
51+
" 20.9, 19.4, 20. , 14. , 30.1, 33.1, 20.6, 22.6, 33.4, 20.1, 10.5,\n",
52+
" 15.6, 16.8, 22.6, 34.6, 19.8, 17.8, 22. , 17.4, 15.4, 16.7, 22.6,\n",
53+
" 15.1, 21.4, 15.3, 7.4, 13.9, 17.6, 25. , 46.7, 17.1, 23.1, 18.7,\n",
54+
" 21.9, 18.9, 26.7, 22.3, 25. , 14.6, 42.8, 17.3, 22.2, 36.5, 22.8,\n",
55+
" 19.9, 36.2, 50. , 25. , 22.2, 17.5, 23.9, 19.6, 24.7, 28.4, 8.7,\n",
56+
" 21.7, 20. , 19.9, 24.5, 15. , 7. , 15.2, 20.4, 8.5, 17.1, 30.1,\n",
57+
" 15. , 19.4, 23.2, 17. , 18.9, 50. , 25. , 46. , 7.2, 17.8, 35.1,\n",
58+
" 24.3, 5. , 16.6, 21.8, 28.5, 22. , 20.3, 21.7, 26.4, 30.7, 50. ,\n",
59+
" 17.2, 26.6, 21. , 23.4, 19.5, 20.7, 23.3, 48.8, 15.6, 19.6, 17.4,\n",
60+
" 21.7, 14.6, 37.9, 9.7, 17.8, 12.1, 20.1, 29.9, 26.4, 18.8, 32.5,\n",
61+
" 15.7, 13.4, 21.7, 23.6, 11.9, 13.8, 22.2, 13. , 33.2, 50. , 22.3,\n",
62+
" 22.4, 23.8, 29.1, 20.8, 23.7, 19.8, 13.9, 28.4, 45.4, 23.7, 50. ,\n",
63+
" 18. , 17.1, 18.9, 10.4, 24.7, 23.9, 23. , 20.2, 8.5, 14.2, 20.3,\n",
64+
" 18.5, 12. , 19.3, 20.6, 16.1, 12.3, 23.1, 22.7, 20.3, 16.7, 27.9,\n",
65+
" 21.4, 8.1, 37.6, 15.6, 29.6, 22.9, 24.8, 24.4, 50. , 28.7, 50. ,\n",
66+
" 16.5, 18.2, 50. , 16.2, 14.1, 21.2, 18.4, 25. , 50. , 21.2, 20.4,\n",
67+
" 15.2, 22. , 19.8, 22.1, 23.9, 24.6, 23.9, 21.7, 44.8, 7.2, 18.5,\n",
68+
" 20.1, 23.3, 19.2, 29.1, 31. , 22.9, 27.5, 39.8, 22. , 22.8, 22.9,\n",
69+
" 14.3, 14.5, 22.4, 19.3, 32. , 20.1, 18.3, 24.5, 18.4, 23.1, 22.6,\n",
70+
" 20.2, 17.8, 31.6, 43.5, 36.4, 11.3, 20.5, 23.2, 29.8, 20.6, 24.3,\n",
71+
" 18.1, 19.1, 21.4, 31.5, 19.2, 14.3, 24.8, 21.1, 18.2, 48.3, 19.4,\n",
72+
" 21.2, 10.9, 27.5, 34.7, 14.4, 22.8, 17.8, 50. , 24.4, 12.8, 30.8,\n",
73+
" 28.2, 25. , 33.1, 27.5, 12.7, 43.1, 13.4, 21.5, 33.4, 23.8, 21. ,\n",
74+
" 26.6, 18.5, 23. , 24.1, 20.5, 32.2, 14.4, 11.8, 19.5, 23.7, 13.2,\n",
75+
" 29. , 18.2, 18.6, 23. , 42.3, 17.2, 16.2, 20. , 30.3, 20.9, 20.4,\n",
76+
" 24.8, 18.7, 16.8, 22.5, 18.8, 23.7, 23.8, 19.6, 20.4, 16.1, 44. ,\n",
77+
" 19.3, 17.4, 10.2, 11.7, 37.2, 11. , 23.6, 22.8, 15. , 34.9, 17.9,\n",
78+
" 24.4, 24.5, 6.3, 29.4, 10.4, 38.7, 20. , 19.4, 37. , 50. , 18.7,\n",
79+
" 48.5, 35.4, 23.4, 7. , 50. , 20.7, 35.4, 9.6, 25.1, 16.1, 27. ,\n",
80+
" 16.6, 13.3, 25. , 24. , 19.6, 29.6, 21.7, 19.1, 22. , 13.3, 27.1,\n",
81+
" 22.9, 33.2, 13.5, 14.5, 8.3, 41.7, 31.2, 23.9, 23.1, 24.3, 18.3,\n",
82+
" 20.8, 28. , 19.5, 21.5, 13.1, 12.5, 31.7, 13.1, 23.1, 14.5, 22.2,\n",
83+
" 13.1, 37.3, 22. , 10.2, 5. , 19.3, 16. , 18.6, 50. , 31.6, 24.1,\n",
84+
" 15.6, 19.4, 23.3, 23.2, 13.6])"
85+
]
86+
},
87+
"execution_count": 8,
88+
"metadata": {},
89+
"output_type": "execute_result"
90+
}
91+
],
92+
"source": [
93+
"X_train\n",
94+
"y_train"
95+
]
96+
},
97+
{
98+
"cell_type": "code",
99+
"execution_count": null,
100+
"metadata": {},
101+
"outputs": [],
102+
"source": []
103+
}
104+
],
105+
"metadata": {
106+
"kernelspec": {
107+
"display_name": "Python 3",
108+
"language": "python",
109+
"name": "python3"
110+
},
111+
"language_info": {
112+
"codemirror_mode": {
113+
"name": "ipython",
114+
"version": 3
115+
},
116+
"file_extension": ".py",
117+
"mimetype": "text/x-python",
118+
"name": "python",
119+
"nbconvert_exporter": "python",
120+
"pygments_lexer": "ipython3",
121+
"version": "3.6.6"
122+
}
123+
},
124+
"nbformat": 4,
125+
"nbformat_minor": 2
126+
}

‎0005DecisionTreeRegressor/.ipynb_checkpoints/sklearn生成决策树并且可视化-checkpoint.ipynb‎

Lines changed: 282 additions & 0 deletions
Large diffs are not rendered by default.

‎0005DecisionTreeRegressor/Untitled.ipynb‎

Lines changed: 126 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,126 @@
1+
{
2+
"cells": [
3+
{
4+
"cell_type": "code",
5+
"execution_count": 1,
6+
"metadata": {},
7+
"outputs": [],
8+
"source": [
9+
"from sklearn.datasets import load_boston\n",
10+
"boston = load_boston()"
11+
]
12+
},
13+
{
14+
"cell_type": "code",
15+
"execution_count": 4,
16+
"metadata": {},
17+
"outputs": [],
18+
"source": [
19+
"import sklearn.model_selection as skm"
20+
]
21+
},
22+
{
23+
"cell_type": "code",
24+
"execution_count": null,
25+
"metadata": {},
26+
"outputs": [],
27+
"source": []
28+
},
29+
{
30+
"cell_type": "code",
31+
"execution_count": 5,
32+
"metadata": {},
33+
"outputs": [],
34+
"source": [
35+
"import numpy as np\n",
36+
"X_train,X_test,y_train,y_test =skm.train_test_split(boston.data,\n",
37+
" boston.target,\n",
38+
" test_size=0.25,\n",
39+
" random_state=33)"
40+
]
41+
},
42+
{
43+
"cell_type": "code",
44+
"execution_count": 8,
45+
"metadata": {},
46+
"outputs": [
47+
{
48+
"data": {
49+
"text/plain": [
50+
"array([33.8, 20.3, 10.2, 22. , 21.2, 24.2, 29. , 22.7, 21.8, 34.9, 25.2,\n",
51+
" 20.9, 19.4, 20. , 14. , 30.1, 33.1, 20.6, 22.6, 33.4, 20.1, 10.5,\n",
52+
" 15.6, 16.8, 22.6, 34.6, 19.8, 17.8, 22. , 17.4, 15.4, 16.7, 22.6,\n",
53+
" 15.1, 21.4, 15.3, 7.4, 13.9, 17.6, 25. , 46.7, 17.1, 23.1, 18.7,\n",
54+
" 21.9, 18.9, 26.7, 22.3, 25. , 14.6, 42.8, 17.3, 22.2, 36.5, 22.8,\n",
55+
" 19.9, 36.2, 50. , 25. , 22.2, 17.5, 23.9, 19.6, 24.7, 28.4, 8.7,\n",
56+
" 21.7, 20. , 19.9, 24.5, 15. , 7. , 15.2, 20.4, 8.5, 17.1, 30.1,\n",
57+
" 15. , 19.4, 23.2, 17. , 18.9, 50. , 25. , 46. , 7.2, 17.8, 35.1,\n",
58+
" 24.3, 5. , 16.6, 21.8, 28.5, 22. , 20.3, 21.7, 26.4, 30.7, 50. ,\n",
59+
" 17.2, 26.6, 21. , 23.4, 19.5, 20.7, 23.3, 48.8, 15.6, 19.6, 17.4,\n",
60+
" 21.7, 14.6, 37.9, 9.7, 17.8, 12.1, 20.1, 29.9, 26.4, 18.8, 32.5,\n",
61+
" 15.7, 13.4, 21.7, 23.6, 11.9, 13.8, 22.2, 13. , 33.2, 50. , 22.3,\n",
62+
" 22.4, 23.8, 29.1, 20.8, 23.7, 19.8, 13.9, 28.4, 45.4, 23.7, 50. ,\n",
63+
" 18. , 17.1, 18.9, 10.4, 24.7, 23.9, 23. , 20.2, 8.5, 14.2, 20.3,\n",
64+
" 18.5, 12. , 19.3, 20.6, 16.1, 12.3, 23.1, 22.7, 20.3, 16.7, 27.9,\n",
65+
" 21.4, 8.1, 37.6, 15.6, 29.6, 22.9, 24.8, 24.4, 50. , 28.7, 50. ,\n",
66+
" 16.5, 18.2, 50. , 16.2, 14.1, 21.2, 18.4, 25. , 50. , 21.2, 20.4,\n",
67+
" 15.2, 22. , 19.8, 22.1, 23.9, 24.6, 23.9, 21.7, 44.8, 7.2, 18.5,\n",
68+
" 20.1, 23.3, 19.2, 29.1, 31. , 22.9, 27.5, 39.8, 22. , 22.8, 22.9,\n",
69+
" 14.3, 14.5, 22.4, 19.3, 32. , 20.1, 18.3, 24.5, 18.4, 23.1, 22.6,\n",
70+
" 20.2, 17.8, 31.6, 43.5, 36.4, 11.3, 20.5, 23.2, 29.8, 20.6, 24.3,\n",
71+
" 18.1, 19.1, 21.4, 31.5, 19.2, 14.3, 24.8, 21.1, 18.2, 48.3, 19.4,\n",
72+
" 21.2, 10.9, 27.5, 34.7, 14.4, 22.8, 17.8, 50. , 24.4, 12.8, 30.8,\n",
73+
" 28.2, 25. , 33.1, 27.5, 12.7, 43.1, 13.4, 21.5, 33.4, 23.8, 21. ,\n",
74+
" 26.6, 18.5, 23. , 24.1, 20.5, 32.2, 14.4, 11.8, 19.5, 23.7, 13.2,\n",
75+
" 29. , 18.2, 18.6, 23. , 42.3, 17.2, 16.2, 20. , 30.3, 20.9, 20.4,\n",
76+
" 24.8, 18.7, 16.8, 22.5, 18.8, 23.7, 23.8, 19.6, 20.4, 16.1, 44. ,\n",
77+
" 19.3, 17.4, 10.2, 11.7, 37.2, 11. , 23.6, 22.8, 15. , 34.9, 17.9,\n",
78+
" 24.4, 24.5, 6.3, 29.4, 10.4, 38.7, 20. , 19.4, 37. , 50. , 18.7,\n",
79+
" 48.5, 35.4, 23.4, 7. , 50. , 20.7, 35.4, 9.6, 25.1, 16.1, 27. ,\n",
80+
" 16.6, 13.3, 25. , 24. , 19.6, 29.6, 21.7, 19.1, 22. , 13.3, 27.1,\n",
81+
" 22.9, 33.2, 13.5, 14.5, 8.3, 41.7, 31.2, 23.9, 23.1, 24.3, 18.3,\n",
82+
" 20.8, 28. , 19.5, 21.5, 13.1, 12.5, 31.7, 13.1, 23.1, 14.5, 22.2,\n",
83+
" 13.1, 37.3, 22. , 10.2, 5. , 19.3, 16. , 18.6, 50. , 31.6, 24.1,\n",
84+
" 15.6, 19.4, 23.3, 23.2, 13.6])"
85+
]
86+
},
87+
"execution_count": 8,
88+
"metadata": {},
89+
"output_type": "execute_result"
90+
}
91+
],
92+
"source": [
93+
"X_train\n",
94+
"y_train"
95+
]
96+
},
97+
{
98+
"cell_type": "code",
99+
"execution_count": null,
100+
"metadata": {},
101+
"outputs": [],
102+
"source": []
103+
}
104+
],
105+
"metadata": {
106+
"kernelspec": {
107+
"display_name": "Python 3",
108+
"language": "python",
109+
"name": "python3"
110+
},
111+
"language_info": {
112+
"codemirror_mode": {
113+
"name": "ipython",
114+
"version": 3
115+
},
116+
"file_extension": ".py",
117+
"mimetype": "text/x-python",
118+
"name": "python",
119+
"nbconvert_exporter": "python",
120+
"pygments_lexer": "ipython3",
121+
"version": "3.6.6"
122+
}
123+
},
124+
"nbformat": 4,
125+
"nbformat_minor": 2
126+
}

‎0005DecisionTreeRegressor/bjGDP.csv‎

Lines changed: 39 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,39 @@
1+
year,gdp
2+
1978,108.8
3+
1979,120.1
4+
1980,139.1
5+
1981,139.2
6+
1982,154.9
7+
1983,183.1
8+
1984,216.6
9+
1985,257.1
10+
1986,284.9
11+
1987,326.8
12+
1988,410.2
13+
1989,456.0
14+
1990,500.8
15+
1991,598.9
16+
1992,709.1
17+
1993,886.2
18+
1994,1145.3
19+
1995,1507.7
20+
1996,1805.0
21+
1997,2096.8
22+
1998,2406.2
23+
1999,2713.5
24+
2000,3212.8
25+
2001,3769.9
26+
2002,4396.0
27+
2003,5104.1
28+
2004,6164.9
29+
2005,7141.4
30+
2006,8312.6
31+
2007,10071.9
32+
2008,11392.0
33+
2009,12419.0
34+
2010,14441.6
35+
2011,16627.9
36+
2012,18350.1
37+
2013,20330.1
38+
2014,21944.1
39+
2015,23685.7

0 commit comments

Comments
 (0)