gpt4 book ai didi

Python实现机器学习算法的分类

转载 作者:qq735679552 更新时间:2022-09-27 22:32:09 24 4
gpt4 key购买 nike

CFSDN坚持开源创造价值,我们致力于搭建一个资源共享平台,让每一个IT人在这里找到属于你的精彩世界.

这篇CFSDN的博客文章Python实现机器学习算法的分类由作者收集整理,如果你对这篇文章有兴趣,记得点赞哟.

Python算法的分类

对葡萄酒数据集进行测试,由于数据集是多分类且数据的样本分布不平衡,所以直接对数据测试,效果不理想。所以使用SMOTE过采样对数据进行处理,对数据去重,去空,处理后数据达到均衡,然后进行测试,与之前测试相比,准确率提升较高.

Python实现机器学习算法的分类

例如:决策树:

Smote处理前:

Python实现机器学习算法的分类

Smote处理后:

Python实现机器学习算法的分类

?
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
from typing import Counter
from matplotlib import colors, markers
import numpy as np
import pandas as pd
import operator
import matplotlib.pyplot as plt
from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC
# 判断模型预测准确率的模型
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import f1_score
from sklearn.metrics import classification_report
 
#设置绘图内的文字
plt.rcParams[ 'font.family' ] = [ 'sans-serif' ]
plt.rcParams[ 'font.sans-serif' ] = [ 'SimHei' ]
 
 
path = "C:\\Users\\zt\\Desktop\\winequality\\myexcel.xls"
# path=r"C:\\Users\\zt\\Desktop\\winequality\\winequality-red.csv"#您要读取的文件路径
# exceldata = np.loadtxt(
#     path,
#     dtype=str,
#     delimiter=";",#每列数据的隔开标志
#     skiprows=1
# )
 
# print(Counter(exceldata[:,-1]))
 
exceldata = pd.read_excel(path)
print (exceldata)
 
print (exceldata[exceldata.duplicated()])
print (exceldata.duplicated(). sum ())
 
#去重
exceldata = exceldata.drop_duplicates()
 
 
#判空去空
print (exceldata.isnull())
print (exceldata.isnull(). sum )
print (exceldata[~exceldata.isnull()])
exceldata = exceldata[~exceldata.isnull()]
 
print (Counter(exceldata[ "quality" ]))
 
#smote
 
#使用imlbearn库中上采样方法中的SMOTE接口
from imblearn.over_sampling import SMOTE
#定义SMOTE模型,random_state相当于随机数种子的作用
 
 
X,y = np.split(exceldata,( 11 ,),axis = 1 )
smo = SMOTE(random_state = 10 )
 
x_smo,y_smo = SMOTE().fit_resample(X.values,y.values)
 
 
 
 
print (Counter(y_smo))
 
 
 
x_smo = pd.DataFrame({ "fixed acidity" :x_smo[:, 0 ], "volatile acidity" :x_smo[:, 1 ], "citric acid" :x_smo[:, 2 ] , "residual sugar" :x_smo[:, 3 ] , "chlorides" :x_smo[:, 4 ], "free sulfur dioxide" :x_smo[:, 5 ] , "total sulfur dioxide" :x_smo[:, 6 ] , "density" :x_smo[:, 7 ], "pH" :x_smo[:, 8 ] , "sulphates" :x_smo[:, 9 ] , " alcohol" :x_smo[:, 10 ]})
y_smo = pd.DataFrame({ "quality" :y_smo})
print (x_smo.shape)
print (y_smo.shape)
#合并
exceldata = pd.concat([x_smo,y_smo],axis = 1 )
print (exceldata)
 
#分割X,y
X,y = np.split(exceldata,( 11 ,),axis = 1 )
X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 10 ,train_size = 0.7 )
print ( "训练集大小:%d" % (X_train.shape[ 0 ]))
print ( "测试集大小:%d" % (X_test.shape[ 0 ]))
 
 
 
def func_mlp(X_train,X_test,y_train,y_test):
     print ( "神经网络MLP:" )
     kk = [i for i in range ( 200 , 500 , 50 ) ] #迭代次数
     t_precision = []
     t_recall = []
     t_accuracy = []
     t_f1_score = []
     for n in kk:
         method = MLPClassifier(activation = "tanh" ,solver = 'lbfgs' , alpha = 1e - 5 ,
                     hidden_layer_sizes = ( 5 , 2 ), random_state = 1 ,max_iter = n)
         method.fit(X_train,y_train)
         MLPClassifier(activation = 'relu' , alpha = 1e - 05 , batch_size = 'auto' , beta_1 = 0.9 ,
                         beta_2 = 0.999 , early_stopping = False , epsilon = 1e - 08 ,
                         hidden_layer_sizes = ( 5 , 2 ), learning_rate = 'constant' ,
                         learning_rate_init = 0.001 , max_iter = n, momentum = 0.9 ,
                         nesterovs_momentum = True , power_t = 0.5 , random_state = 1 , shuffle = True ,
                         solver = 'lbfgs' , tol = 0.0001 , validation_fraction = 0.1 , verbose = False ,
                         warm_start = False )
         y_predict = method.predict(X_test)
         t = classification_report(y_test, y_predict, target_names = [ '3' , '4' , '5' , '6' , '7' , '8' ],output_dict = True )
         print (t)
         t_accuracy.append(t[ "accuracy" ])
         t_precision.append(t[ "weighted avg" ][ "precision" ])
         t_recall.append(t[ "weighted avg" ][ "recall" ])
         t_f1_score.append(t[ "weighted avg" ][ "f1-score" ])
     plt.figure( "数据未处理MLP" )
     plt.subplot( 2 , 2 , 1 )
     #添加文本 #x轴文本
     plt.xlabel( '迭代次数' )
     #y轴文本
     plt.ylabel( 'accuracy' )
     #标题
     plt.title( '不同迭代次数下的accuracy' )
     plt.plot(kk,t_accuracy,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 2 )
     #添加文本 #x轴文本
     plt.xlabel( '迭代次数' )
     #y轴文本
     plt.ylabel( 'precision' )
     #标题
     plt.title( '不同迭代次数下的precision' )
     plt.plot(kk,t_precision,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 3 )
     #添加文本 #x轴文本
     plt.xlabel( '迭代次数' )
     #y轴文本
     plt.ylabel( 'recall' )
     #标题
     plt.title( '不同迭代次数下的recall' )
     plt.plot(kk,t_recall,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 4 )
     #添加文本 #x轴文本
     plt.xlabel( '迭代次数' )
     #y轴文本
     plt.ylabel( 'f1_score' )
     #标题
     plt.title( '不同迭代次数下的f1_score' )
     plt.plot(kk,t_f1_score,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.show()
 
 
def func_svc(X_train,X_test,y_train,y_test):
     print ( "向量机:" )
     kk = [ "linear" , "poly" , "rbf" ] #核函数类型
     t_precision = []
     t_recall = []
     t_accuracy = []
     t_f1_score = []
     for n in kk:
         method = SVC(kernel = n, random_state = 0 )
         method = method.fit(X_train, y_train)
         y_predic = method.predict(X_test)
         t = classification_report(y_test, y_predic, target_names = [ '3' , '4' , '5' , '6' , '7' , '8' ],output_dict = True )
         print (t)
         t_accuracy.append(t[ "accuracy" ])
         t_precision.append(t[ "weighted avg" ][ "precision" ])
         t_recall.append(t[ "weighted avg" ][ "recall" ])
         t_f1_score.append(t[ "weighted avg" ][ "f1-score" ])
     plt.figure( "数据未处理向量机" )
     plt.subplot( 2 , 2 , 1 )
     #添加文本 #x轴文本
     plt.xlabel( '核函数类型' )
     #y轴文本
     plt.ylabel( 'accuracy' )
     #标题
     plt.title( '不同核函数类型下的accuracy' )
     plt.plot(kk,t_accuracy,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 2 )
     #添加文本 #x轴文本
     plt.xlabel( '核函数类型' )
     #y轴文本
     plt.ylabel( 'precision' )
     #标题
     plt.title( '不同核函数类型下的precision' )
     plt.plot(kk,t_precision,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 3 )
     #添加文本 #x轴文本
     plt.xlabel( '核函数类型' )
     #y轴文本
     plt.ylabel( 'recall' )
     #标题
     plt.title( '不同核函数类型下的recall' )
     plt.plot(kk,t_recall,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 4 )
     #添加文本 #x轴文本
     plt.xlabel( '核函数类型' )
     #y轴文本
     plt.ylabel( 'f1_score' )
     #标题
     plt.title( '不同核函数类型下的f1_score' )
     plt.plot(kk,t_f1_score,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.show()
 
def func_classtree(X_train,X_test,y_train,y_test):
     print ( "决策树:" )
     kk = [ 10 , 20 , 30 , 40 , 50 , 60 , 70 , 80 , 90 , 100 ] #决策树最大深度
     t_precision = []
     t_recall = []
     t_accuracy = []
     t_f1_score = []
     for n in kk:
         method = tree.DecisionTreeClassifier(criterion = "gini" ,max_depth = n)
         method.fit(X_train,y_train)
         predic = method.predict(X_test)
         print ( "method.predict:%f" % method.score(X_test,y_test))
 
        
         t = classification_report(y_test, predic, target_names = [ '3' , '4' , '5' , '6' , '7' , '8' ],output_dict = True )
         print (t)
         t_accuracy.append(t[ "accuracy" ])
         t_precision.append(t[ "weighted avg" ][ "precision" ])
         t_recall.append(t[ "weighted avg" ][ "recall" ])
         t_f1_score.append(t[ "weighted avg" ][ "f1-score" ])
     plt.figure( "数据未处理决策树" )
     plt.subplot( 2 , 2 , 1 )
     #添加文本 #x轴文本
     plt.xlabel( '决策树最大深度' )
     #y轴文本
     plt.ylabel( 'accuracy' )
     #标题
     plt.title( '不同决策树最大深度下的accuracy' )
     plt.plot(kk,t_accuracy,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 2 )
     #添加文本 #x轴文本
     plt.xlabel( '决策树最大深度' )
     #y轴文本
     plt.ylabel( 'precision' )
     #标题
     plt.title( '不同决策树最大深度下的precision' )
     plt.plot(kk,t_precision,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 3 )
     #添加文本 #x轴文本
     plt.xlabel( '决策树最大深度' )
     #y轴文本
     plt.ylabel( 'recall' )
     #标题
     plt.title( '不同决策树最大深度下的recall' )
     plt.plot(kk,t_recall,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 4 )
     #添加文本 #x轴文本
     plt.xlabel( '决策树最大深度' )
     #y轴文本
     plt.ylabel( 'f1_score' )
     #标题
     plt.title( '不同决策树最大深度下的f1_score' )
     plt.plot(kk,t_f1_score,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.show()
 
def func_adaboost(X_train,X_test,y_train,y_test):
     print ( "提升树:" )
     kk = [ 0.1 , 0.2 , 0.3 , 0.4 , 0.5 , 0.6 , 0.7 , 0.8 ]
     t_precision = []
     t_recall = []
     t_accuracy = []
     t_f1_score = []
     for n in range ( 100 , 200 , 200 ):
         for k in kk:
             print ( "迭代次数为:%d\n学习率:%.2f" % (n,k))
             bdt = AdaBoostClassifier(tree.DecisionTreeClassifier(max_depth = 2 , min_samples_split = 20 ),
                                     algorithm = "SAMME" ,
                                     n_estimators = n, learning_rate = k)
             bdt.fit(X_train, y_train)
             #迭代100次 ,学习率为0.1
             y_pred = bdt.predict(X_test)
             print ( "训练集score:%lf" % (bdt.score(X_train,y_train)))
             print ( "测试集score:%lf" % (bdt.score(X_test,y_test)))
             print (bdt.feature_importances_)
 
             t = classification_report(y_test, y_pred, target_names = [ '3' , '4' , '5' , '6' , '7' , '8' ],output_dict = True )
             print (t)
             t_accuracy.append(t[ "accuracy" ])
             t_precision.append(t[ "weighted avg" ][ "precision" ])
             t_recall.append(t[ "weighted avg" ][ "recall" ])
             t_f1_score.append(t[ "weighted avg" ][ "f1-score" ])
     plt.figure( "数据未处理迭代100次(adaboost)" )
     plt.subplot( 2 , 2 , 1 )
     #添加文本 #x轴文本
     plt.xlabel( '学习率' )
     #y轴文本
     plt.ylabel( 'accuracy' )
     #标题
     plt.title( '不同学习率下的accuracy' )
     plt.plot(kk,t_accuracy,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 2 )
     #添加文本 #x轴文本
     plt.xlabel( '学习率' )
     #y轴文本
     plt.ylabel( 'precision' )
     #标题
     plt.title( '不同学习率下的precision' )
     plt.plot(kk,t_precision,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 3 )
     #添加文本 #x轴文本
     plt.xlabel( '学习率' )
     #y轴文本
     plt.ylabel( 'recall' )
     #标题
     plt.title( '不同学习率下的recall' )
     plt.plot(kk,t_recall,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 4 )
     #添加文本 #x轴文本
     plt.xlabel( '学习率' )
     #y轴文本
     plt.ylabel( 'f1_score' )
     #标题
     plt.title( '不同学习率下的f1_score' )
     plt.plot(kk,t_f1_score,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.show()
 
 
# inX 用于分类的输入向量
# dataSet表示训练样本集
# 标签向量为labels,标签向量的元素数目和矩阵dataSet的行数相同
# 参数k表示选择最近邻居的数目
def classify0(inx, data_set, labels, k):
     """实现k近邻"""
     data_set_size = data_set.shape[ 0 ]   # 数据集个数,即行数
     diff_mat = np.tile(inx, (data_set_size, 1 )) - data_set   # 各个属性特征做差
     sq_diff_mat = diff_mat * * 2  # 各个差值求平方
     sq_distances = sq_diff_mat. sum (axis = 1 # 按行求和
     distances = sq_distances * * 0.5   # 开方
     sorted_dist_indicies = distances.argsort()  # 按照从小到大排序,并输出相应的索引值
     class_count = {}  # 创建一个字典,存储k个距离中的不同标签的数量
 
     for i in range (k):
         vote_label = labels[sorted_dist_indicies[i]]  # 求出第i个标签
 
         # 访问字典中值为vote_label标签的数值再加1,
         #class_count.get(vote_label, 0)中的0表示当为查询到vote_label时的默认值
         class_count[vote_label[ 0 ]] = class_count.get(vote_label[ 0 ], 0 ) + 1
     # 将获取的k个近邻的标签类进行排序
     sorted_class_count = sorted (class_count.items(),
     key = operator.itemgetter( 1 ), reverse = True )
     # 标签类最多的就是未知数据的类
     return sorted_class_count[ 0 ][ 0 ]
 
def func_knn(X_train,X_test,y_train,y_test):
     print ( "k近邻:" )
     kk = [i for i in range ( 3 , 30 , 5 )] #k的取值
     t_precision = []
     t_recall = []
     t_accuracy = []
     t_f1_score = []
     for n in kk:
         y_predict = []
         for x in X_test.values:
             a = classify0(x, X_train.values, y_train.values, n)  # 调用k近邻分类
             y_predict.append(a)
 
         t = classification_report(y_test, y_predict, target_names = [ '3' , '4' , '5' , '6' , '7' , '8' ],output_dict = True )
         print (t)
         t_accuracy.append(t[ "accuracy" ])
         t_precision.append(t[ "weighted avg" ][ "precision" ])
         t_recall.append(t[ "weighted avg" ][ "recall" ])
         t_f1_score.append(t[ "weighted avg" ][ "f1-score" ])
     plt.figure( "数据未处理k近邻" )
     plt.subplot( 2 , 2 , 1 )
     #添加文本 #x轴文本
     plt.xlabel( 'k值' )
     #y轴文本
     plt.ylabel( 'accuracy' )
     #标题
     plt.title( '不同k值下的accuracy' )
     plt.plot(kk,t_accuracy,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
    
     plt.subplot( 2 , 2 , 2 )
     #添加文本 #x轴文本
     plt.xlabel( 'k值' )
     #y轴文本
     plt.ylabel( 'precision' )
     #标题
     plt.title( '不同k值下的precision' )
     plt.plot(kk,t_precision,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 3 )
     #添加文本 #x轴文本
     plt.xlabel( 'k值' )
     #y轴文本
     plt.ylabel( 'recall' )
     #标题
     plt.title( '不同k值下的recall' )
     plt.plot(kk,t_recall,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 4 )
     #添加文本 #x轴文本
     plt.xlabel( 'k值' )
     #y轴文本
     plt.ylabel( 'f1_score' )
     #标题
     plt.title( '不同k值下的f1_score' )
     plt.plot(kk,t_f1_score,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.show()
 
def func_randomforest(X_train,X_test,y_train,y_test):
     print ( "随机森林:" )
     t_precision = []
     t_recall = []
     t_accuracy = []
     t_f1_score = []
     kk = [ 10 , 20 , 30 , 40 , 50 , 60 , 70 , 80 ] #默认树的数量
     for n in kk:
         clf = RandomForestClassifier(n_estimators = n, max_depth = 100 ,min_samples_split = 2 , random_state = 10 ,verbose = True )
         clf.fit(X_train,y_train)
         predic = clf.predict(X_test)
 
         print ( "特征重要性:" ,clf.feature_importances_)
         print ( "acc:" ,clf.score(X_test,y_test))
 
         t = classification_report(y_test, predic, target_names = [ '3' , '4' , '5' , '6' , '7' , '8' ],output_dict = True )
         print (t)
         t_accuracy.append(t[ "accuracy" ])
         t_precision.append(t[ "weighted avg" ][ "precision" ])
         t_recall.append(t[ "weighted avg" ][ "recall" ])
         t_f1_score.append(t[ "weighted avg" ][ "f1-score" ])
     plt.figure( "数据未处理深度100(随机森林)" )
     plt.subplot( 2 , 2 , 1 )
     #添加文本 #x轴文本
     plt.xlabel( '树的数量' )
     #y轴文本
     plt.ylabel( 'accuracy' )
     #标题
     plt.title( '不同树的数量下的accuracy' )
     plt.plot(kk,t_accuracy,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
    
     plt.subplot( 2 , 2 , 2 )
     #添加文本 #x轴文本
     plt.xlabel( '树的数量' )
     #y轴文本
     plt.ylabel( 'precision' )
     #标题
     plt.title( '不同树的数量下的precision' )
     plt.plot(kk,t_precision,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 3 )
     #添加文本 #x轴文本
     plt.xlabel( '树的数量' )
     #y轴文本
     plt.ylabel( 'recall' )
     #标题
     plt.title( '不同树的数量下的recall' )
     plt.plot(kk,t_recall,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.subplot( 2 , 2 , 4 )
     #添加文本 #x轴文本
     plt.xlabel( '树的数量' )
     #y轴文本
     plt.ylabel( 'f1_score' )
     #标题
     plt.title( '不同树的数量下的f1_score' )
     plt.plot(kk,t_f1_score,color = "r" ,marker = "o" ,lineStyle = "-" )
     plt.yticks(np.arange( 0 , 1 , 0.1 ))
 
     plt.show()
 
 
 
 
 
 
if __name__ = = '__main__' :
     #神经网络
     print (func_mlp(X_train,X_test,y_train,y_test))
     #向量机
     print (func_svc(X_train,X_test,y_train,y_test))
     #决策树
     print (func_classtree(X_train,X_test,y_train,y_test))
     #提升树
     print (func_adaboost(X_train,X_test,y_train,y_test))
     #knn
     print (func_knn(X_train,X_test,y_train,y_test))
     #randomforest
     print (func_randomforest(X_train,X_test,y_train,y_test))

  。

到此这篇关于Python实现机器学习算法的分类的文章就介绍到这了,更多相关Python算法分类内容请搜索我以前的文章或继续浏览下面的相关文章希望大家以后多多支持我! 。

原文链接:https://blog.csdn.net/qq_41934789/article/details/117400996 。

最后此篇关于Python实现机器学习算法的分类的文章就讲到这里了,如果你想了解更多关于Python实现机器学习算法的分类的内容请搜索CFSDN的文章或继续浏览相关文章,希望大家以后支持我的博客! 。

24 4 0
Copyright 2021 - 2024 cfsdn All Rights Reserved 蜀ICP备2022000587号
广告合作:1813099741@qq.com 6ren.com