bagging与randomforest是集成学习中的两个比较出名的算法， 特点是都可以并行。本文根据UCI 的glass数据集，使用sklear的RandomForestClassifier和BaggingClassifier对玻璃杯进行分类， 并评估学习器数量变化的时候，两个算法在该数据集上的准确率的变化
- 数据集
- 数据集比较小，直接贴上来：
1,1.52101,13.64,4.49,1.10,71.78,0.06,8.75,0.00,0.00,1
2,1.51761,13.89,3.60,1.36,72.73,0.48,7.83,0.00,0.00,1
3,1.51618,13.53,3.55,1.54,72.99,0.39,7.78,0.00,0.00,1
4,1.51766,13.21,3.69,1.29,72.61,0.57,8.22,0.00,0.00,1
5,1.51742,13.27,3.62,1.24,73.08,0.55,8.07,0.00,0.00,1
6,1.51596,12.79,3.61,1.62,72.97,0.64,8.07,0.00,0.26,1
7,1.51743,13.30,3.60,1.14,73.09,0.58,8.17,0.00,0.00,1
8,1.51756,13.15,3.61,1.05,73.24,0.57,8.24,0.00,0.00,1
9,1.51918,14.04,3.58,1.37,72.08,0.56,8.30,0.00,0.00,1
10,1.51755,13.00,3.60,1.36,72.99,0.57,8.40,0.00,0.11,1
11,1.51571,12.72,3.46,1.56,73.20,0.67,8.09,0.00,0.24,1
12,1.51763,12.80,3.66,1.27,73.01,0.60,8.56,0.00,0.00,1
13,1.51589,12.88,3.43,1.40,73.28,0.69,8.05,0.00,0.24,1
14,1.51748,12.86,3.56,1.27,73.21,0.54,8.38,0.00,0.17,1
15,1.51763,12.61,3.59,1.31,73.29,0.58,8.50,0.00,0.00,1
16,1.51761,12.81,3.54,1.23,73.24,0.58,8.39,0.00,0.00,1
17,1.51784,12.68,3.67,1.16,73.11,0.61,8.70,0.00,0.00,1
18,1.52196,14.36,3.85,0.89,71.36,0.15,9.15,0.00,0.00,1
19,1.51911,13.90,3.73,1.18,72.12,0.06,8.89,0.00,0.00,1
20,1.51735,13.02,3.54,1.69,72.73,0.54,8.44,0.00,0.07,1
21,1.51750,12.82,3.55,1.49,72.75,0.54,8.52,0.00,0.19,1
22,1.51966,14.77,3.75,0.29,72.02,0.03,9.00,0.00,0.00,1
23,1.51736,12.78,3.62,1.29,72.79,0.59,8.70,0.00,0.00,1
24,1.51751,12.81,3.57,1.35,73.02,0.62,8.59,0.00,0.00,1
25,1.51720,13.38,3.50,1.15,72.85,0.50,8.43,0.00,0.00,1
26,1.51764,12.98,3.54,1.21,73.00,0.65,8.53,0.00,0.00,1
27,1.51793,13.21,3.48,1.41,72.64,0.59,8.43,0.00,0.00,1
28,1.51721,12.87,3.48,1.33,73.04,0.56,8.43,0.00,0.00,1
29,1.51768,12.56,3.52,1.43,73.15,0.57,8.54,0.00,0.00,1
30,1.51784,13.08,3.49,1.28,72.86,0.60,8.49,0.00,0.00,1
31,1.51768,12.65,3.56,1.30,73.08,0.61,8.69,0.00,0.14,1
32,1.51747,12.84,3.50,1.14,73.27,0.56,8.55,0.00,0.00,1
33,1.51775,12.85,3.48,1.23,72.97,0.61,8.56,0.09,0.22,1
34,1.51753,12.57,3.47,1.38,73.39,0.60,8.55,0.00,0.06,1
35,1.51783,12.69,3.54,1.34,72.95,0.57,8.75,0.00,0.00,1
36,1.51567,13.29,3.45,1.21,72.74,0.56,8.57,0.00,0.00,1
37,1.51909,13.89,3.53,1.32,71.81,0.51,8.78,0.11,0.00,1
38,1.51797,12.74,3.48,1.35,72.96,0.64,8.68,0.00,0.00,1
39,1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0.00,0.00,1
40,1.52213,14.21,3.82,0.47,71.77,0.11,9.57,0.00,0.00,1
41,1.51793,12.79,3.50,1.12,73.03,0.64,8.77,0.00,0.00,1
42,1.51755,12.71,3.42,1.20,73.20,0.59,8.64,0.00,0.00,1
43,1.51779,13.21,3.39,1.33,72.76,0.59,8.59,0.00,0.00,1
44,1.52210,13.73,3.84,0.72,71.76,0.17,9.74,0.00,0.00,1
45,1.51786,12.73,3.43,1.19,72.95,0.62,8.76,0.00,0.30,1
46,1.51900,13.49,3.48,1.35,71.95,0.55,9.00,0.00,0.00,1
47,1.51869,13.19,3.37,1.18,72.72,0.57,8.83,0.00,0.16,1
48,1.52667,13.99,3.70,0.71,71.57,0.02,9.82,0.00,0.10,1
49,1.52223,13.21,3.77,0.79,71.99,0.13,10.02,0.00,0.00,1
50,1.51898,13.58,3.35,1.23,72.08,0.59,8.91,0.00,0.00,1
51,1.52320,13.72,3.72,0.51,71.75,0.09,10.06,0.00,0.16,1
52,1.51926,13.20,3.33,1.28,72.36,0.60,9.14,0.00,0.11,1
53,1.51808,13.43,2.87,1.19,72.84,0.55,9.03,0.00,0.00,1
54,1.51837,13.14,2.84,1.28,72.85,0.55,9.07,0.00,0.00,1
55,1.51778,13.21,2.81,1.29,72.98,0.51,9.02,0.00,0.09,1
56,1.51769,12.45,2.71,1.29,73.70,0.56,9.06,0.00,0.24,1
57,1.51215,12.99,3.47,1.12,72.98,0.62,8.35,0.00,0.31,1
58,1.51824,12.87,3.48,1.29,72.95,0.60,8.43,0.00,0.00,1
59,1.51754,13.48,3.74,1.17,72.99,0.59,8.03,0.00,0.00,1
60,1.51754,13.39,3.66,1.19,72.79,0.57,8.27,0.00,0.11,1
61,1.51905,13.60,3.62,1.11,72.64,0.14,8.76,0.00,0.00,1
62,1.51977,13.81,3.58,1.32,71.72,0.12,8.67,0.69,0.00,1
63,1.52172,13.51,3.86,0.88,71.79,0.23,9.54,0.00,0.11,1
64,1.52227,14.17,3.81,0.78,71.35,0.00,9.69,0.00,0.00,1
65,1.52172,13.48,3.74,0.90,72.01,0.18,9.61,0.00,0.07,1
66,1.52099,13.69,3.59,1.12,71.96,0.09,9.40,0.00,0.00,1
67,1.52152,13.05,3.65,0.87,72.22,0.19,9.85,0.00,0.17,1
68,1.52152,13.05,3.65,0.87,72.32,0.19,9.85,0.00,0.17,1
69,1.52152,13.12,3.58,0.90,72.20,0.23,9.82,0.00,0.16,1
70,1.52300,13.31,3.58,0.82,71.99,0.12,10.17,0.00,0.03,1
71,1.51574,14.86,3.67,1.74,71.87,0.16,7.36,0.00,0.12,2
72,1.51848,13.64,3.87,1.27,71.96,0.54,8.32,0.00,0.32,2
73,1.51593,13.09,3.59,1.52,73.10,0.67,7.83,0.00,0.00,2
74,1.51631,13.34,3.57,1.57,72.87,0.61,7.89,0.00,0.00,2
75,1.51596,13.02,3.56,1.54,73.11,0.72,7.90,0.00,0.00,2
76,1.51590,13.02,3.58,1.51,73.12,0.69,7.96,0.00,0.00,2
77,1.51645,13.44,3.61,1.54,72.39,0.66,8.03,0.00,0.00,2
78,1.51627,13.00,3.58,1.54,72.83,0.61,8.04,0.00,0.00,2
79,1.51613,13.92,3.52,1.25,72.88,0.37,7.94,0.00,0.14,2
80,1.51590,12.82,3.52,1.90,72.86,0.69,7.97,0.00,0.00,2
81,1.51592,12.86,3.52,2.12,72.66,0.69,7.97,0.00,0.00,2
82,1.51593,13.25,3.45,1.43,73.17,0.61,7.86,0.00,0.00,2
83,1.51646,13.41,3.55,1.25,72.81,0.68,8.10,0.00,0.00,2
84,1.51594,13.09,3.52,1.55,72.87,0.68,8.05,0.00,0.09,2
85,1.51409,14.25,3.09,2.08,72.28,1.10,7.08,0.00,0.00,2
86,1.51625,13.36,3.58,1.49,72.72,0.45,8.21,0.00,0.00,2
87,1.51569,13.24,3.49,1.47,73.25,0.38,8.03,0.00,0.00,2
88,1.51645,13.40,3.49,1.52,72.65,0.67,8.08,0.00,0.10,2
89,1.51618,13.01,3.50,1.48,72.89,0.60,8.12,0.00,0.00,2
90,1.51640,12.55,3.48,1.87,73.23,0.63,8.08,0.00,0.09,2
91,1.51841,12.93,3.74,1.11,72.28,0.64,8.96,0.00,0.22,2
92,1.51605,12.90,3.44,1.45,73.06,0.44,8.27,0.00,0.00,2
93,1.51588,13.12,3.41,1.58,73.26,0.07,8.39,0.00,0.19,2
94,1.51590,13.24,3.34,1.47,73.10,0.39,8.22,0.00,0.00,2
95,1.51629,12.71,3.33,1.49,73.28,0.67,8.24,0.00,0.00,2
96,1.51860,13.36,3.43,1.43,72.26,0.51,8.60,0.00,0.00,2
97,1.51841,13.02,3.62,1.06,72.34,0.64,9.13,0.00,0.15,2
98,1.51743,12.20,3.25,1.16,73.55,0.62,8.90,0.00,0.24,2
99,1.51689,12.67,2.88,1.71,73.21,0.73,8.54,0.00,0.00,2
100,1.51811,12.96,2.96,1.43,72.92,0.60,8.79,0.14,0.00,2
101,1.51655,12.75,2.85,1.44,73.27,0.57,8.79,0.11,0.22,2
102,1.51730,12.35,2.72,1.63,72.87,0.70,9.23,0.00,0.00,2
103,1.51820,12.62,2.76,0.83,73.81,0.35,9.42,0.00,0.20,2
104,1.52725,13.80,3.15,0.66,70.57,0.08,11.64,0.00,0.00,2
105,1.52410,13.83,2.90,1.17,71.15,0.08,10.79,0.00,0.00,2
106,1.52475,11.45,0.00,1.88,72.19,0.81,13.24,0.00,0.34,2
107,1.53125,10.73,0.00,2.10,69.81,0.58,13.30,3.15,0.28,2
108,1.53393,12.30,0.00,1.00,70.16,0.12,16.19,0.00,0.24,2
109,1.52222,14.43,0.00,1.00,72.67,0.10,11.52,0.00,0.08,2
110,1.51818,13.72,0.00,0.56,74.45,0.00,10.99,0.00,0.00,2
111,1.52664,11.23,0.00,0.77,73.21,0.00,14.68,0.00,0.00,2
112,1.52739,11.02,0.00,0.75,73.08,0.00,14.96,0.00,0.00,2
113,1.52777,12.64,0.00,0.67,72.02,0.06,14.40,0.00,0.00,2
114,1.51892,13.46,3.83,1.26,72.55,0.57,8.21,0.00,0.14,2
115,1.51847,13.10,3.97,1.19,72.44,0.60,8.43,0.00,0.00,2
116,1.51846,13.41,3.89,1.33,72.38,0.51,8.28,0.00,0.00,2
117,1.51829,13.24,3.90,1.41,72.33,0.55,8.31,0.00,0.10,2
118,1.51708,13.72,3.68,1.81,72.06,0.64,7.88,0.00,0.00,2
119,1.51673,13.30,3.64,1.53,72.53,0.65,8.03,0.00,0.29,2
120,1.51652,13.56,3.57,1.47,72.45,0.64,7.96,0.00,0.00,2
121,1.51844,13.25,3.76,1.32,72.40,0.58,8.42,0.00,0.00,2
122,1.51663,12.93,3.54,1.62,72.96,0.64,8.03,0.00,0.21,2
123,1.51687,13.23,3.54,1.48,72.84,0.56,8.10,0.00,0.00,2
124,1.51707,13.48,3.48,1.71,72.52,0.62,7.99,0.00,0.00,2
125,1.52177,13.20,3.68,1.15,72.75,0.54,8.52,0.00,0.00,2
126,1.51872,12.93,3.66,1.56,72.51,0.58,8.55,0.00,0.12,2
127,1.51667,12.94,3.61,1.26,72.75,0.56,8.60,0.00,0.00,2
128,1.52081,13.78,2.28,1.43,71.99,0.49,9.85,0.00,0.17,2
129,1.52068,13.55,2.09,1.67,72.18,0.53,9.57,0.27,0.17,2
130,1.52020,13.98,1.35,1.63,71.76,0.39,10.56,0.00,0.18,2
131,1.52177,13.75,1.01,1.36,72.19,0.33,11.14,0.00,0.00,2
132,1.52614,13.70,0.00,1.36,71.24,0.19,13.44,0.00,0.10,2
133,1.51813,13.43,3.98,1.18,72.49,0.58,8.15,0.00,0.00,2
134,1.51800,13.71,3.93,1.54,71.81,0.54,8.21,0.00,0.15,2
135,1.51811,13.33,3.85,1.25,72.78,0.52,8.12,0.00,0.00,2
136,1.51789,13.19,3.90,1.30,72.33,0.55,8.44,0.00,0.28,2
137,1.51806,13.00,3.80,1.08,73.07,0.56,8.38,0.00,0.12,2
138,1.51711,12.89,3.62,1.57,72.96,0.61,8.11,0.00,0.00,2
139,1.51674,12.79,3.52,1.54,73.36,0.66,7.90,0.00,0.00,2
140,1.51674,12.87,3.56,1.64,73.14,0.65,7.99,0.00,0.00,2
141,1.51690,13.33,3.54,1.61,72.54,0.68,8.11,0.00,0.00,2
142,1.51851,13.20,3.63,1.07,72.83,0.57,8.41,0.09,0.17,2
143,1.51662,12.85,3.51,1.44,73.01,0.68,8.23,0.06,0.25,2
144,1.51709,13.00,3.47,1.79,72.72,0.66,8.18,0.00,0.00,2
145,1.51660,12.99,3.18,1.23,72.97,0.58,8.81,0.00,0.24,2
146,1.51839,12.85,3.67,1.24,72.57,0.62,8.68,0.00,0.35,2
147,1.51769,13.65,3.66,1.11,72.77,0.11,8.60,0.00,0.00,3
148,1.51610,13.33,3.53,1.34,72.67,0.56,8.33,0.00,0.00,3
149,1.51670,13.24,3.57,1.38,72.70,0.56,8.44,0.00,0.10,3
150,1.51643,12.16,3.52,1.35,72.89,0.57,8.53,0.00,0.00,3
151,1.51665,13.14,3.45,1.76,72.48,0.60,8.38,0.00,0.17,3
152,1.52127,14.32,3.90,0.83,71.50,0.00,9.49,0.00,0.00,3
153,1.51779,13.64,3.65,0.65,73.00,0.06,8.93,0.00,0.00,3
154,1.51610,13.42,3.40,1.22,72.69,0.59,8.32,0.00,0.00,3
155,1.51694,12.86,3.58,1.31,72.61,0.61,8.79,0.00,0.00,3
156,1.51646,13.04,3.40,1.26,73.01,0.52,8.58,0.00,0.00,3
157,1.51655,13.41,3.39,1.28,72.64,0.52,8.65,0.00,0.00,3
158,1.52121,14.03,3.76,0.58,71.79,0.11,9.65,0.00,0.00,3
159,1.51776,13.53,3.41,1.52,72.04,0.58,8.79,0.00,0.00,3
160,1.51796,13.50,3.36,1.63,71.94,0.57,8.81,0.00,0.09,3
161,1.51832,13.33,3.34,1.54,72.14,0.56,8.99,0.00,0.00,3
162,1.51934,13.64,3.54,0.75,72.65,0.16,8.89,0.15,0.24,3
163,1.52211,14.19,3.78,0.91,71.36,0.23,9.14,0.00,0.37,3
164,1.51514,14.01,2.68,3.50,69.89,1.68,5.87,2.20,0.00,5
165,1.51915,12.73,1.85,1.86,72.69,0.60,10.09,0.00,0.00,5
166,1.52171,11.56,1.88,1.56,72.86,0.47,11.41,0.00,0.00,5
167,1.52151,11.03,1.71,1.56,73.44,0.58,11.62,0.00,0.00,5
168,1.51969,12.64,0.00,1.65,73.75,0.38,11.53,0.00,0.00,5
169,1.51666,12.86,0.00,1.83,73.88,0.97,10.17,0.00,0.00,5
170,1.51994,13.27,0.00,1.76,73.03,0.47,11.32,0.00,0.00,5
171,1.52369,13.44,0.00,1.58,72.22,0.32,12.24,0.00,0.00,5
172,1.51316,13.02,0.00,3.04,70.48,6.21,6.96,0.00,0.00,5
173,1.51321,13.00,0.00,3.02,70.70,6.21,6.93,0.00,0.00,5
174,1.52043,13.38,0.00,1.40,72.25,0.33,12.50,0.00,0.00,5
175,1.52058,12.85,1.61,2.17,72.18,0.76,9.70,0.24,0.51,5
176,1.52119,12.97,0.33,1.51,73.39,0.13,11.27,0.00,0.28,5
177,1.51905,14.00,2.39,1.56,72.37,0.00,9.57,0.00,0.00,6
178,1.51937,13.79,2.41,1.19,72.76,0.00,9.77,0.00,0.00,6
179,1.51829,14.46,2.24,1.62,72.38,0.00,9.26,0.00,0.00,6
180,1.51852,14.09,2.19,1.66,72.67,0.00,9.32,0.00,0.00,6
181,1.51299,14.40,1.74,1.54,74.55,0.00,7.59,0.00,0.00,6
182,1.51888,14.99,0.78,1.74,72.50,0.00,9.95,0.00,0.00,6
183,1.51916,14.15,0.00,2.09,72.74,0.00,10.88,0.00,0.00,6
184,1.51969,14.56,0.00,0.56,73.48,0.00,11.22,0.00,0.00,6
185,1.51115,17.38,0.00,0.34,75.41,0.00,6.65,0.00,0.00,6
186,1.51131,13.69,3.20,1.81,72.81,1.76,5.43,1.19,0.00,7
187,1.51838,14.32,3.26,2.22,71.25,1.46,5.79,1.63,0.00,7
188,1.52315,13.44,3.34,1.23,72.38,0.60,8.83,0.00,0.00,7
189,1.52247,14.86,2.20,2.06,70.26,0.76,9.76,0.00,0.00,7
190,1.52365,15.79,1.83,1.31,70.43,0.31,8.61,1.68,0.00,7
191,1.51613,13.88,1.78,1.79,73.10,0.00,8.67,0.76,0.00,7
192,1.51602,14.85,0.00,2.38,73.28,0.00,8.76,0.64,0.09,7
193,1.51623,14.20,0.00,2.79,73.46,0.04,9.04,0.40,0.09,7
194,1.51719,14.75,0.00,2.00,73.02,0.00,8.53,1.59,0.08,7
195,1.51683,14.56,0.00,1.98,73.29,0.00,8.52,1.57,0.07,7
196,1.51545,14.14,0.00,2.68,73.39,0.08,9.07,0.61,0.05,7
197,1.51556,13.87,0.00,2.54,73.23,0.14,9.41,0.81,0.01,7
198,1.51727,14.70,0.00,2.34,73.28,0.00,8.95,0.66,0.00,7
199,1.51531,14.38,0.00,2.66,73.10,0.04,9.08,0.64,0.00,7
200,1.51609,15.01,0.00,2.51,73.05,0.05,8.83,0.53,0.00,7
201,1.51508,15.15,0.00,2.25,73.50,0.00,8.34,0.63,0.00,7
202,1.51653,11.95,0.00,1.19,75.18,2.70,8.93,0.00,0.00,7
203,1.51514,14.85,0.00,2.42,73.72,0.00,8.39,0.56,0.00,7
204,1.51658,14.80,0.00,1.99,73.11,0.00,8.28,1.71,0.00,7
205,1.51617,14.95,0.00,2.27,73.30,0.00,8.71,0.67,0.00,7
206,1.51732,14.95,0.00,1.80,72.99,0.00,8.61,1.55,0.00,7
207,1.51645,14.94,0.00,1.87,73.11,0.00,8.67,1.38,0.00,7
208,1.51831,14.39,0.00,1.82,72.86,1.41,6.47,2.88,0.00,7
209,1.51640,14.37,0.00,2.74,72.85,0.00,9.45,0.54,0.00,7
210,1.51623,14.14,0.00,2.88,72.61,0.08,9.18,1.06,0.00,7
211,1.51685,14.92,0.00,1.99,73.06,0.00,8.40,1.59,0.00,7
212,1.52065,14.36,0.00,2.02,73.42,0.00,8.44,1.64,0.00,7
213,1.51651,14.38,0.00,1.94,73.61,0.00,8.48,1.57,0.00,7
214,1.51711,14.23,0.00,2.08,73.36,0.00,8.62,1.67,0.00,7
```
- 数据集使用说明:
- 最后一列是分类：
```
Type of glass: (class attribute)
-- 1 building_windows_float_processed
-- 2 building_windows_non_float_processed
-- 3 vehicle_windows_float_processed
-- 4 vehicle_windows_non_float_processed (none in this database)
-- 5 containers
-- 6 tableware
-- 7 headlamps
```
- 代码如下：
# -*- coding: utf - 8 - *-
import sklearn as sk
import numpy as np
import sklearn as sk
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils import shuffle
from sklearn.ensemble import BaggingClassifier
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import seaborn as sns
import itertools
palette = itertools.cycle(sns.color_palette())
Max_Classfiers = 1000
def read_file(file,one_hot=False):
np_file = np.genfromtxt(fname=file,delimiter=',', \
# dtype=None
)
if one_hot:
pass
else:
return np_file
if __name__ == '__main__':
np_file = read_file('./glass.txt')
##very important
np.random.shuffle(np_file)
file_len = np_file.shape[0]
train_num = round(file_len * 0.7)
##get random index sample
# train_sample = np_file[0:train_num]
# train_sample_data = train_sample[:,0:-1]
# train_sample_label = train_sample[:,-1]
train_sample_data = np_file[:,0:-1]
train_sample_label = np_file[:,-1]
train_sample_label.astype(np.int16)
##get test sample
# test_sample = np_file[train_num:]
# test_sample_data = test_sample[:,0:-1]
# test_sample_label = test_sample[:,-1]
# test_sample_label.astype(np.int16)
print(file_len,train_num)
X, y = shuffle(train_sample_data,train_sample_label,random_state=3)
print(X.size,y.size)
params = {'max_features':'auto', 'max_depth': 6, 'min_samples_split': 2, \
'oob_score':True}
rf_errors = []
for x in range(1,Max_Classfiers):
params['n_estimators'] = x
params['n_jobs'] = x
forest = RandomForestClassifier(**params)
forest.fit(X,y)
# rf_error = 1.0 - forest.score(test_sample_data,test_sample_label)
rf_error = 1.0 - forest.oob_score_
rf_errors.append(rf_error)
print(rf_errors)
X, y = shuffle(train_sample_data,train_sample_label,random_state=3)
print(X.size,y.size)
bg_params = {'max_features':1,'max_samples':1, 'bootstrap_features':True, \
'oob_score':True,'n_jobs':-1}
bagging_errors = []
for x in range(1,Max_Classfiers):
bg_params['n_estimators'] = x
bg_params['n_jobs'] = x
bagging = BaggingClassifier(**bg_params)
bagging.fit(X,y)
# bagging_error = 1.0 - bagging.score(test_sample_data,test_sample_label)
bagging_error = 1.0 -bagging.oob_score_
bagging_errors.append(bagging_error)
print(bagging_errors)
##draw picture
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(range(1,Max_Classfiers),rf_errors,linestyle='-',color=next(palette),label='Random Forest Error')
ax.plot(range(1,Max_Classfiers),bagging_errors,linestyle='-',color=next(palette),label='Bagging Error')
ax.set_ylim((0.0, 1.0))
ax.set_xlabel('n_estimators')
ax.set_ylabel('error rate')
leg = ax.legend(loc='upper right', fancybox=True)
leg.get_frame().set_alpha(0.7)
plt.show()
- 随着基分类器的数量增加，错误率如下图所示：
- 总结：
- randomforest性能比bagging好，实际发现计算速度也要快一些
- bagging使用的是决策树，理论上性能不应该比random forest差距这么大，因为randoforest也是决策树为基分类器的。目前还没有发现原因，有明白的朋友可以告知一下。
