第4章下最基础的分类算法-k近邻算法 kNN

4-6 网格搜索与k近邻算法中更多超参数

Notbook 示例

Notbook 源码

  1 [1]
  2 import numpy as np
  3 from sklearn import datasets
  4 [2]
  5 digits = datasets.load_digits()
  6 X = digits.data
  7 y = digits.target
  8 [3]
  9 from sklearn.model_selection import train_test_split
 10 
 11 X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3 ,random_state=111 )
 12 [4]
 13 from sklearn.neighbors import KNeighborsClassifier
 14 
 15 knn_clf = KNeighborsClassifier( n_neighbors = 6 )
 16 knn_clf.fit(X_train,y_train)
 17 knn_clf.score(X_test,y_test)
 18 0.9833333333333333
 19 Grid Search
 20 [5]
 21 param_gid = [
 22     {
 23         'weights': ['unifrom'],
 24         'n_neighbors': [ i for i in range(1,11)]
 25     },
 26     {
 27         'weights': ['distance'],
 28         'n_neighbors': [ i for i in range(1,11)],
 29         'p': [ i for i in range(1,6)]
 30     }
 31     
 32 ]
 33 [6]
 34 knn_clf = KNeighborsClassifier()
 35 [7]
 36 from sklearn.model_selection import GridSearchCV
 37 
 38 grid_search = GridSearchCV(knn_clf,param_gid)
 39 [8]
 40 %%time
 41 grid_search.fit(X_train,y_train)
 42 CPU times: total: 2min 15s
 43 Wall time: 2min 18s
 44 
 45 F:\anaconda\lib\site-packages\sklearn\model_selection\_validation.py:372: FitFailedWarning: 
 46 50 fits failed out of a total of 300.
 47 The score on these train-test partitions for these parameters will be set to nan.
 48 If these failures are not expected, you can try to debug them by setting error_score='raise'.
 49 
 50 Below are more details about the failures:
 51 --------------------------------------------------------------------------------
 52 50 fits failed with the following error:
 53 Traceback (most recent call last):
 54   File "F:\anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 680, in _fit_and_score
 55     estimator.fit(X_train, y_train, **fit_params)
 56   File "F:\anaconda\lib\site-packages\sklearn\neighbors\_classification.py", line 196, in fit
 57     self.weights = _check_weights(self.weights)
 58   File "F:\anaconda\lib\site-packages\sklearn\neighbors\_base.py", line 82, in _check_weights
 59     raise ValueError(
 60 ValueError: weights not recognized: should be 'uniform', 'distance', or a callable function
 61 
 62   warnings.warn(some_fits_failed_message, FitFailedWarning)
 63 F:\anaconda\lib\site-packages\sklearn\model_selection\_search.py:969: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan
 64         nan        nan        nan        nan 0.98011446 0.98965724
 65  0.98965724 0.99204452 0.98966041 0.98090811 0.98965724 0.98965724
 66  0.99204452 0.98966041 0.98408904 0.98726997 0.98646999 0.98726681
 67  0.98488585 0.98249542 0.98806678 0.98886359 0.98886359 0.98726997
 68  0.98249542 0.98647948 0.98966041 0.98726997 0.98488902 0.98249542
 69  0.98488585 0.9856795  0.98885727 0.9856795  0.98090179 0.98329539
 70  0.9856795  0.98806362 0.98488269 0.98010181 0.98170176 0.9856795
 71  0.98726997 0.98487637 0.97692405 0.98408904 0.98329223 0.98647948
 72  0.98488585 0.97851135 0.98010814 0.98488269 0.98726997 0.98726997]
 73   warnings.warn(
 74 
 75 GridSearchCV(estimator=KNeighborsClassifier(),
 76              param_grid=[{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
 77                           'weights': ['unifrom']},
 78                          {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
 79                           'p': [1, 2, 3, 4, 5], 'weights': ['distance']}])
 80 [9]
 81 grid_search.best_estimator_
 82 KNeighborsClassifier(n_neighbors=1, p=4, weights='distance')
 83 [10]
 84 grid_search.best_score_
 85 0.9920445203313729
 86 [11]
 87 grid_search.best_params_
 88 {'n_neighbors': 1, 'p': 4, 'weights': 'distance'}
 89 若 随机数为111，则 grid_search.best_score_ = 0.9920445203313729， grid_search.best_params_ = {'n_neighbors': 1, 'p': 4, 'weights': 'distance'}
 90 
 91 [12]
 92 knn_clf = grid_search.best_estimator_
 93 [13]
 94 knn_clf.predict(X_test)
 95 array([7, 1, 2, 9, 5, 8, 6, 4, 8, 3, 8, 4, 4, 5, 7, 1, 6, 1, 0, 6, 6, 8,
 96        8, 0, 8, 4, 5, 8, 0, 0, 3, 3, 5, 2, 1, 4, 8, 6, 7, 3, 3, 9, 6, 0,
 97        4, 9, 7, 3, 8, 7, 4, 3, 5, 0, 3, 1, 7, 6, 5, 7, 6, 0, 9, 7, 7, 8,
 98        2, 8, 6, 6, 1, 1, 2, 6, 4, 6, 4, 8, 6, 9, 8, 1, 3, 4, 4, 2, 0, 7,
 99        6, 0, 8, 2, 0, 5, 8, 5, 3, 3, 7, 4, 7, 3, 4, 2, 4, 9, 1, 8, 5, 1,
100        2, 7, 0, 2, 8, 9, 7, 5, 7, 7, 8, 8, 9, 2, 3, 9, 7, 7, 8, 2, 5, 3,
101        2, 4, 0, 1, 4, 8, 7, 9, 6, 8, 1, 5, 2, 6, 1, 4, 1, 6, 5, 3, 4, 2,
102        2, 7, 0, 7, 1, 5, 4, 6, 1, 7, 4, 9, 6, 8, 5, 8, 4, 3, 3, 2, 5, 6,
103        7, 9, 0, 2, 0, 5, 4, 8, 0, 8, 6, 9, 7, 3, 1, 9, 4, 2, 7, 9, 4, 0,
104        5, 2, 8, 2, 9, 1, 8, 5, 4, 5, 7, 7, 5, 5, 0, 1, 4, 4, 6, 5, 7, 6,
105        0, 6, 7, 1, 9, 0, 6, 1, 2, 9, 1, 5, 3, 0, 2, 1, 0, 9, 3, 4, 1, 0,
106        9, 9, 2, 0, 5, 3, 6, 5, 5, 3, 9, 1, 2, 8, 7, 4, 9, 8, 8, 1, 3, 1,
107        6, 3, 0, 7, 2, 4, 7, 2, 5, 0, 6, 4, 7, 4, 1, 0, 3, 1, 8, 0, 5, 6,
108        9, 5, 5, 0, 6, 0, 5, 2, 9, 7, 2, 9, 1, 0, 3, 5, 8, 8, 0, 4, 3, 4,
109        6, 1, 6, 1, 7, 3, 3, 2, 3, 6, 7, 1, 0, 1, 9, 6, 6, 6, 8, 2, 3, 5,
110        9, 4, 4, 5, 3, 9, 7, 1, 3, 0, 0, 8, 6, 9, 7, 9, 6, 4, 2, 7, 2, 6,
111        5, 4, 1, 7, 9, 0, 1, 1, 7, 5, 3, 3, 7, 4, 9, 0, 8, 6, 0, 9, 1, 9,
112        7, 8, 8, 8, 6, 2, 1, 3, 0, 2, 3, 6, 8, 1, 6, 1, 3, 9, 6, 2, 5, 2,
113        9, 7, 7, 6, 5, 8, 0, 1, 8, 6, 3, 5, 0, 4, 3, 9, 9, 3, 4, 3, 7, 9,
114        2, 3, 5, 3, 9, 3, 1, 4, 7, 7, 1, 7, 4, 3, 0, 8, 0, 9, 6, 3, 9, 8,
115        3, 9, 9, 9, 4, 1, 6, 7, 7, 2, 0, 1, 0, 7, 5, 7, 6, 1, 5, 0, 6, 9,
116        5, 1, 2, 1, 7, 5, 2, 1, 8, 1, 8, 8, 2, 8, 6, 8, 7, 0, 9, 9, 6, 2,
117        0, 9, 6, 3, 4, 3, 0, 8, 5, 4, 8, 6, 4, 5, 2, 5, 6, 1, 0, 5, 7, 0,
118        9, 5, 3, 2, 9, 3, 0, 6, 4, 8, 3, 2, 3, 6, 6, 8, 1, 9, 4, 3, 1, 1,
119        4, 5, 4, 3, 7, 5, 3, 3, 7, 8, 1, 0])
120 [14]
121 knn_clf.score(X_test,y_test)
122 0.9907407407407407
123 [15]
124 %%time
125 grid_search = GridSearchCV(knn_clf,param_gid,n_jobs= 4, verbose = 2)
126 grid_search.fit(X_train,y_train)
127 # 创建多个分类器来比较，可以并行处理，n_jobs 为分配核的数量,默认为单核 1 .-1为全核。
128 # verbose，及时输出一些信息，值越大越详细
129 Fitting 5 folds for each of 60 candidates, totalling 300 fits
130 CPU times: total: 484 ms
131 Wall time: 1min 28s
132 
133 F:\anaconda\lib\site-packages\sklearn\model_selection\_validation.py:372: FitFailedWarning: 
134 50 fits failed out of a total of 300.
135 The score on these train-test partitions for these parameters will be set to nan.
136 If these failures are not expected, you can try to debug them by setting error_score='raise'.
137 
138 Below are more details about the failures:
139 --------------------------------------------------------------------------------
140 50 fits failed with the following error:
141 Traceback (most recent call last):
142   File "F:\anaconda\lib\site-packages\sklearn\model_selection\_validation.py", line 680, in _fit_and_score
143     estimator.fit(X_train, y_train, **fit_params)
144   File "F:\anaconda\lib\site-packages\sklearn\neighbors\_classification.py", line 196, in fit
145     self.weights = _check_weights(self.weights)
146   File "F:\anaconda\lib\site-packages\sklearn\neighbors\_base.py", line 82, in _check_weights
147     raise ValueError(
148 ValueError: weights not recognized: should be 'uniform', 'distance', or a callable function
149 
150   warnings.warn(some_fits_failed_message, FitFailedWarning)
151 F:\anaconda\lib\site-packages\sklearn\model_selection\_search.py:969: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan
152         nan        nan        nan        nan 0.98011446 0.98965724
153  0.98965724 0.99204452 0.98966041 0.98090811 0.98965724 0.98965724
154  0.99204452 0.98966041 0.98408904 0.98726997 0.98646999 0.98726681
155  0.98488585 0.98249542 0.98806678 0.98886359 0.98886359 0.98726997
156  0.98249542 0.98647948 0.98966041 0.98726997 0.98488902 0.98249542
157  0.98488585 0.9856795  0.98885727 0.9856795  0.98090179 0.98329539
158  0.9856795  0.98806362 0.98488269 0.98010181 0.98170176 0.9856795
159  0.98726997 0.98487637 0.97692405 0.98408904 0.98329223 0.98647948
160  0.98488585 0.97851135 0.98010814 0.98488269 0.98726997 0.98726997]
161   warnings.warn(
162 
163 GridSearchCV(estimator=KNeighborsClassifier(n_neighbors=1, p=4,
164                                             weights='distance'),
165              n_jobs=4,
166              param_grid=[{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
167                           'weights': ['unifrom']},
168                          {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
169                           'p': [1, 2, 3, 4, 5], 'weights': ['distance']}],
170              verbose=2)

4-7 数据归一化

Notbook 示例

Notbook 源码

  1 数据归一化处理
  2 [1]
  3 import numpy as np
  4 import matplotlib.pyplot as plt
  5 最值归一化 Normalization
  6 [2]
  7 x = np.random.randint(0,100, size = 100)
  8 [3]
  9 x
 10 array([ 2, 58, 55, 40, 68,  7, 72,  4, 50, 89, 96, 19, 71,  6, 41, 40, 63,
 11         4,  2, 26, 79, 62, 62, 92, 49, 46, 75, 47, 44, 91, 40, 67, 38, 14,
 12        13,  0, 93, 15, 20, 50, 38, 31, 41, 22, 36, 85, 64, 87, 98, 65, 31,
 13        10, 46, 22, 86, 24, 68, 25, 11, 31, 22, 87, 84, 18, 58, 87,  4, 15,
 14        64, 92, 70, 59, 74, 81, 47, 33,  1, 93,  6, 37, 62, 17, 58, 56, 98,
 15        53,  2, 70, 36, 17, 21, 66,  1, 79, 60, 71, 89, 71, 61, 30])
 16 [5]
 17 ( x - np.min(x)) / (np.max(x) - np.min(x))
 18 array([0.02040816, 0.59183673, 0.56122449, 0.40816327, 0.69387755,
 19        0.07142857, 0.73469388, 0.04081633, 0.51020408, 0.90816327,
 20        0.97959184, 0.19387755, 0.7244898 , 0.06122449, 0.41836735,
 21        0.40816327, 0.64285714, 0.04081633, 0.02040816, 0.26530612,
 22        0.80612245, 0.63265306, 0.63265306, 0.93877551, 0.5       ,
 23        0.46938776, 0.76530612, 0.47959184, 0.44897959, 0.92857143,
 24        0.40816327, 0.68367347, 0.3877551 , 0.14285714, 0.13265306,
 25        0.        , 0.94897959, 0.15306122, 0.20408163, 0.51020408,
 26        0.3877551 , 0.31632653, 0.41836735, 0.2244898 , 0.36734694,
 27        0.86734694, 0.65306122, 0.8877551 , 1.        , 0.66326531,
 28        0.31632653, 0.10204082, 0.46938776, 0.2244898 , 0.87755102,
 29        0.24489796, 0.69387755, 0.25510204, 0.1122449 , 0.31632653,
 30        0.2244898 , 0.8877551 , 0.85714286, 0.18367347, 0.59183673,
 31        0.8877551 , 0.04081633, 0.15306122, 0.65306122, 0.93877551,
 32        0.71428571, 0.60204082, 0.75510204, 0.82653061, 0.47959184,
 33        0.33673469, 0.01020408, 0.94897959, 0.06122449, 0.37755102,
 34        0.63265306, 0.17346939, 0.59183673, 0.57142857, 1.        ,
 35        0.54081633, 0.02040816, 0.71428571, 0.36734694, 0.17346939,
 36        0.21428571, 0.67346939, 0.01020408, 0.80612245, 0.6122449 ,
 37        0.7244898 , 0.90816327, 0.7244898 , 0.62244898, 0.30612245])
 38 [6]
 39 X = np.random.randint(0,100,(50,2))
 40 [7]
 41 X[:10,:]
 42 array([[19, 14],
 43        [23, 82],
 44        [ 4, 17],
 45        [44, 58],
 46        [23, 91],
 47        [46, 17],
 48        [34, 25],
 49        [29, 39],
 50        [69, 61],
 51        [70, 25]])
 52 [9]
 53 X = np.array(X,dtype = float)
 54 [10]
 55 X[:10,:]
 56 array([[19., 14.],
 57        [23., 82.],
 58        [ 4., 17.],
 59        [44., 58.],
 60        [23., 91.],
 61        [46., 17.],
 62        [34., 25.],
 63        [29., 39.],
 64        [69., 61.],
 65        [70., 25.]])
 66 [18]
 67 X[:,0] = (X[:,0] - np.min(X[:,0])) / ( np.max(X[:,0]) - np.min(X[:,0]))
 68 [19]
 69 X[:,1] = (X[:,1] - np.min(X[:,1])) / ( np.max(X[:,1]) - np.min(X[:,1]))
 70 [20]
 71 X[:10,:]
 72 array([[0.19191919, 0.11458333],
 73        [0.23232323, 0.82291667],
 74        [0.04040404, 0.14583333],
 75        [0.44444444, 0.57291667],
 76        [0.23232323, 0.91666667],
 77        [0.46464646, 0.14583333],
 78        [0.34343434, 0.22916667],
 79        [0.29292929, 0.375     ],
 80        [0.6969697 , 0.60416667],
 81        [0.70707071, 0.22916667]])
 82 [21]
 83 plt.scatter(X[:,0],X[:,1])
 84 <matplotlib.collections.PathCollection at 0x1f514d5c2e0>
 85 
 86 [22]
 87 np.mean(X[:,0])
 88 0.4503030303030303
 89 [26]
 90 np.std(X[:,0])
 91 0.32224653972392703
 92 [24]
 93 np.mean(X[:,1])
 94 0.4503030303030303
 95 [27]
 96 np.std(X[:,1])
 97 0.3004160887650475
 98 均值方差归一化
 99 [28]
100 X2  =  np.random.randint(0,100,(50,2))
101 [29]
102 X2 = np.array(X2,dtype = float)
103 [36]
104 X2[:,0] = (X2[:,0] - np.mean(X2[:,0])) /  np.std(X2[:,0])
105 [37]
106 X2[:,1] = (X2[:,1] - np.mean(X2[:,1])) /  np.std(X2[:,1])
107 [38]
108 plt.scatter(X2[:,0],X2[:,1])
109 <matplotlib.collections.PathCollection at 0x1f517fa1430>
110 
111 [39]
112 np.mean(X2[:,0])
113 0.0
114 [40]
115 np.std(X2[:,0])
116 1.0
117 [41]
118 np.mean(X2[:,1])
119 -4.4408920985006264e-17
120 [42]
121 np.std(X2[:,1])
122 1.0

4-8 scikit-learn中的Scaler

Notbook 示例

notbook 源码

  1 [1]
  2 import numpy as np
  3 from sklearn import datasets
  4 [2]
  5 iris = datasets.load_iris()
  6 [3]
  7 X = iris.data
  8 y = iris.target
  9 [4]
 10 X[:10,:]
 11 array([[5.1, 3.5, 1.4, 0.2],
 12        [4.9, 3. , 1.4, 0.2],
 13        [4.7, 3.2, 1.3, 0.2],
 14        [4.6, 3.1, 1.5, 0.2],
 15        [5. , 3.6, 1.4, 0.2],
 16        [5.4, 3.9, 1.7, 0.4],
 17        [4.6, 3.4, 1.4, 0.3],
 18        [5. , 3.4, 1.5, 0.2],
 19        [4.4, 2.9, 1.4, 0.2],
 20        [4.9, 3.1, 1.5, 0.1]])
 21 [5]
 22 from sklearn.model_selection import train_test_split
 23 X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3 ,random_state=666 )
 24 scikit-learn中的StandardScaler
 25 [6]
 26 from sklearn.preprocessing import StandardScaler
 27 [7]
 28 standardScaler = StandardScaler()
 29 [8]
 30 standardScaler.fit(X_train)
 31 StandardScaler()
 32 [9]
 33 standardScaler.mean_
 34 array([5.81619048, 3.08761905, 3.66952381, 1.15714286])
 35 [10]
 36 standardScaler.scale_ #   .std 旧形式已经弃用 ， scale表示数据分布范围
 37 array([0.80747977, 0.43789436, 1.76166176, 0.75464998])
 38 [11]
 39 standardScaler.transform(X_train)
 40 array([[-0.63926119,  1.39846731, -1.2315212 , -1.26832688],
 41        [-1.01078752,  0.94173615, -1.17475662, -0.73827982],
 42        [-1.75384019, -0.42845732, -1.28828579, -1.26832688],
 43        [-0.02005063, -0.88518848,  0.13082885,  0.05679076],
 44        [-0.7631033 ,  0.71337057, -1.28828579, -1.26832688],
 45        [-1.50615597,  0.71337057, -1.28828579, -1.13581511],
 46        [ 0.84684415,  0.25663941,  0.81200388,  1.11688486],
 47        [-0.14389274, -0.42845732,  0.30112261,  0.18930252],
 48        [ 0.97068626, -0.20009175,  0.41465178,  0.32181428],
 49        [ 0.2276336 , -0.42845732,  0.47141637,  0.45432605],
 50        [-1.38231385,  0.25663941, -1.17475662, -1.26832688],
 51        [-1.13462963,  1.17010173, -1.28828579, -1.40083864],
 52        [ 1.09452838,  0.02827383,  1.09582681,  1.64693191],
 53        [ 0.59915993, -0.88518848,  0.69847471,  0.85186133],
 54        [ 0.35147571, -0.6568229 ,  0.58494554,  0.05679076],
 55        [ 0.47531782, -0.6568229 ,  0.64171013,  0.85186133],
 56        [-0.14389274,  2.99702636, -1.2315212 , -1.00330335],
 57        [ 0.59915993, -1.34191964,  0.69847471,  0.45432605],
 58        [ 0.72300204, -0.42845732,  0.3578872 ,  0.18930252],
 59        [-0.88694541,  1.62683289, -1.00446286, -1.00330335],
 60        [ 1.21837049, -0.6568229 ,  0.64171013,  0.32181428],
 61        [-0.88694541,  0.94173615, -1.28828579, -1.13581511],
 62        [-1.8776823 , -0.20009175, -1.45857955, -1.40083864],
 63        [ 0.10379148, -0.20009175,  0.81200388,  0.85186133],
 64        [ 0.72300204, -0.6568229 ,  1.09582681,  1.24939662],
 65        [-0.26773485, -0.6568229 ,  0.69847471,  1.11688486],
 66        [-0.39157696, -1.57028522,  0.01729968, -0.20823277],
 67        [ 1.3422126 ,  0.02827383,  0.69847471,  0.45432605],
 68        [ 0.59915993,  0.71337057,  1.09582681,  1.64693191],
 69        [ 0.84684415, -0.20009175,  1.20935598,  1.38190839],
 70        [-0.14389274,  1.62683289, -1.11799203, -1.13581511],
 71        [ 0.97068626, -0.42845732,  0.52818096,  0.18930252],
 72        [ 1.09452838,  0.48500499,  1.1525914 ,  1.77944368],
 73        [-1.25847174, -0.20009175, -1.28828579, -1.40083864],
 74        [-1.01078752,  1.17010173, -1.28828579, -1.26832688],
 75        [ 0.2276336 , -0.20009175,  0.64171013,  0.85186133],
 76        [-1.01078752, -0.20009175, -1.17475662, -1.26832688],
 77        [ 0.35147571, -0.20009175,  0.69847471,  0.85186133],
 78        [ 0.72300204,  0.02827383,  1.03906223,  0.85186133],
 79        [-0.88694541,  1.39846731, -1.2315212 , -1.00330335],
 80        [-0.14389274, -0.20009175,  0.30112261,  0.05679076],
 81        [-1.01078752,  0.94173615, -1.34505037, -1.13581511],
 82        [-0.88694541,  1.62683289, -1.2315212 , -1.13581511],
 83        [-1.50615597,  0.25663941, -1.28828579, -1.26832688],
 84        [-0.51541907, -0.20009175,  0.47141637,  0.45432605],
 85        [ 0.84684415, -0.6568229 ,  0.52818096,  0.45432605],
 86        [ 0.35147571, -0.6568229 ,  0.18759344,  0.18930252],
 87        [-1.25847174,  0.71337057, -1.17475662, -1.26832688],
 88        [-0.88694541,  0.48500499, -1.11799203, -0.87079159],
 89        [-0.02005063, -0.88518848,  0.81200388,  0.9843731 ],
 90        [-0.26773485, -0.20009175,  0.24435803,  0.18930252],
 91        [ 0.59915993, -0.6568229 ,  0.81200388,  0.45432605],
 92        [ 1.09452838,  0.48500499,  1.1525914 ,  1.24939662],
 93        [ 1.71373893, -0.20009175,  1.20935598,  0.58683781],
 94        [ 1.09452838, -0.20009175,  0.86876847,  1.51442015],
 95        [-1.13462963,  0.02827383, -1.2315212 , -1.40083864],
 96        [-1.13462963, -1.34191964,  0.47141637,  0.71934957],
 97        [-0.14389274, -1.34191964,  0.7552393 ,  1.11688486],
 98        [-1.13462963, -1.57028522, -0.20975866, -0.20823277],
 99        [-0.39157696, -1.57028522,  0.07406427, -0.07572101],
100        [ 1.09452838, -1.34191964,  1.20935598,  0.85186133],
101        [ 0.84684415, -0.20009175,  1.03906223,  0.85186133],
102        [-0.14389274, -1.11355406, -0.09622949, -0.20823277],
103        [ 0.2276336 , -2.02701638,  0.7552393 ,  0.45432605],
104        [ 1.09452838,  0.02827383,  0.58494554,  0.45432605],
105        [-1.13462963,  0.02827383, -1.2315212 , -1.26832688],
106        [ 0.59915993, -1.34191964,  0.7552393 ,  0.9843731 ],
107        [-1.38231385,  0.25663941, -1.34505037, -1.26832688],
108        [ 0.2276336 , -0.88518848,  0.81200388,  0.58683781],
109        [-0.02005063, -1.11355406,  0.18759344,  0.05679076],
110        [ 1.3422126 ,  0.25663941,  1.1525914 ,  1.51442015],
111        [-1.75384019, -0.20009175, -1.34505037, -1.26832688],
112        [ 1.58989682, -0.20009175,  1.26612057,  1.24939662],
113        [ 1.21837049,  0.25663941,  1.26612057,  1.51442015],
114        [-0.7631033 ,  0.94173615, -1.2315212 , -1.26832688],
115        [ 2.58063371,  1.62683289,  1.5499435 ,  1.11688486],
116        [ 0.72300204, -0.6568229 ,  1.09582681,  1.38190839],
117        [-0.26773485, -0.42845732, -0.0394649 ,  0.18930252],
118        [-0.39157696,  2.5402952 , -1.28828579, -1.26832688],
119        [-1.25847174, -0.20009175, -1.28828579, -1.13581511],
120        [ 0.59915993, -0.42845732,  1.09582681,  0.85186133],
121        [-1.75384019,  0.25663941, -1.34505037, -1.26832688],
122        [-0.51541907,  1.85519847, -1.11799203, -1.00330335],
123        [-1.01078752,  0.71337057, -1.17475662, -1.00330335],
124        [ 1.09452838, -0.20009175,  0.7552393 ,  0.71934957],
125        [-0.51541907,  1.85519847, -1.34505037, -1.00330335],
126        [ 2.33294949, -0.6568229 ,  1.72023726,  1.11688486],
127        [-0.26773485, -0.88518848,  0.30112261,  0.18930252],
128        [ 1.21837049, -0.20009175,  1.03906223,  1.24939662],
129        [-0.39157696,  0.94173615, -1.34505037, -1.26832688],
130        [-1.25847174,  0.71337057, -1.00446286, -1.26832688],
131        [-0.51541907,  0.71337057, -1.11799203, -1.26832688],
132        [ 2.33294949,  1.62683289,  1.72023726,  1.38190839],
133        [ 1.3422126 ,  0.02827383,  0.98229764,  1.24939662],
134        [-0.26773485, -1.34191964,  0.13082885, -0.07572101],
135        [-0.88694541,  0.71337057, -1.2315212 , -1.26832688],
136        [-0.88694541,  1.62683289, -1.17475662, -1.26832688],
137        [ 0.35147571, -0.42845732,  0.58494554,  0.32181428],
138        [-0.02005063,  2.08356405, -1.40181496, -1.26832688],
139        [-1.01078752, -2.48374754, -0.09622949, -0.20823277],
140        [ 0.72300204,  0.25663941,  0.47141637,  0.45432605],
141        [ 0.35147571, -0.20009175,  0.52818096,  0.32181428],
142        [ 0.10379148,  0.25663941,  0.64171013,  0.85186133],
143        [ 0.2276336 , -2.02701638,  0.18759344, -0.20823277],
144        [ 1.96142316, -0.6568229 ,  1.37964974,  0.9843731 ]])
145 [12]
146 X_train
147 array([[5.3, 3.7, 1.5, 0.2],
148        [5. , 3.5, 1.6, 0.6],
149        [4.4, 2.9, 1.4, 0.2],
150        [5.8, 2.7, 3.9, 1.2],
151        [5.2, 3.4, 1.4, 0.2],
152        [4.6, 3.4, 1.4, 0.3],
153        [6.5, 3.2, 5.1, 2. ],
154        [5.7, 2.9, 4.2, 1.3],
155        [6.6, 3. , 4.4, 1.4],
156        [6. , 2.9, 4.5, 1.5],
157        [4.7, 3.2, 1.6, 0.2],
158        [4.9, 3.6, 1.4, 0.1],
159        [6.7, 3.1, 5.6, 2.4],
160        [6.3, 2.7, 4.9, 1.8],
161        [6.1, 2.8, 4.7, 1.2],
162        [6.2, 2.8, 4.8, 1.8],
163        [5.7, 4.4, 1.5, 0.4],
164        [6.3, 2.5, 4.9, 1.5],
165        [6.4, 2.9, 4.3, 1.3],
166        [5.1, 3.8, 1.9, 0.4],
167        [6.8, 2.8, 4.8, 1.4],
168        [5.1, 3.5, 1.4, 0.3],
169        [4.3, 3. , 1.1, 0.1],
170        [5.9, 3. , 5.1, 1.8],
171        [6.4, 2.8, 5.6, 2.1],
172        [5.6, 2.8, 4.9, 2. ],
173        [5.5, 2.4, 3.7, 1. ],
174        [6.9, 3.1, 4.9, 1.5],
175        [6.3, 3.4, 5.6, 2.4],
176        [6.5, 3. , 5.8, 2.2],
177        [5.7, 3.8, 1.7, 0.3],
178        [6.6, 2.9, 4.6, 1.3],
179        [6.7, 3.3, 5.7, 2.5],
180        [4.8, 3. , 1.4, 0.1],
181        [5. , 3.6, 1.4, 0.2],
182        [6. , 3. , 4.8, 1.8],
183        [5. , 3. , 1.6, 0.2],
184        [6.1, 3. , 4.9, 1.8],
185        [6.4, 3.1, 5.5, 1.8],
186        [5.1, 3.7, 1.5, 0.4],
187        [5.7, 3. , 4.2, 1.2],
188        [5. , 3.5, 1.3, 0.3],
189        [5.1, 3.8, 1.5, 0.3],
190        [4.6, 3.2, 1.4, 0.2],
191        [5.4, 3. , 4.5, 1.5],
192        [6.5, 2.8, 4.6, 1.5],
193        [6.1, 2.8, 4. , 1.3],
194        [4.8, 3.4, 1.6, 0.2],
195        [5.1, 3.3, 1.7, 0.5],
196        [5.8, 2.7, 5.1, 1.9],
197        [5.6, 3. , 4.1, 1.3],
198        [6.3, 2.8, 5.1, 1.5],
199        [6.7, 3.3, 5.7, 2.1],
200        [7.2, 3. , 5.8, 1.6],
201        [6.7, 3. , 5.2, 2.3],
202        [4.9, 3.1, 1.5, 0.1],
203        [4.9, 2.5, 4.5, 1.7],
204        [5.7, 2.5, 5. , 2. ],
205        [4.9, 2.4, 3.3, 1. ],
206        [5.5, 2.4, 3.8, 1.1],
207        [6.7, 2.5, 5.8, 1.8],
208        [6.5, 3. , 5.5, 1.8],
209        [5.7, 2.6, 3.5, 1. ],
210        [6. , 2.2, 5. , 1.5],
211        [6.7, 3.1, 4.7, 1.5],
212        [4.9, 3.1, 1.5, 0.2],
213        [6.3, 2.5, 5. , 1.9],
214        [4.7, 3.2, 1.3, 0.2],
215        [6. , 2.7, 5.1, 1.6],
216        [5.8, 2.6, 4. , 1.2],
217        [6.9, 3.2, 5.7, 2.3],
218        [4.4, 3. , 1.3, 0.2],
219        [7.1, 3. , 5.9, 2.1],
220        [6.8, 3.2, 5.9, 2.3],
221        [5.2, 3.5, 1.5, 0.2],
222        [7.9, 3.8, 6.4, 2. ],
223        [6.4, 2.8, 5.6, 2.2],
224        [5.6, 2.9, 3.6, 1.3],
225        [5.5, 4.2, 1.4, 0.2],
226        [4.8, 3. , 1.4, 0.3],
227        [6.3, 2.9, 5.6, 1.8],
228        [4.4, 3.2, 1.3, 0.2],
229        [5.4, 3.9, 1.7, 0.4],
230        [5. , 3.4, 1.6, 0.4],
231        [6.7, 3. , 5. , 1.7],
232        [5.4, 3.9, 1.3, 0.4],
233        [7.7, 2.8, 6.7, 2. ],
234        [5.6, 2.7, 4.2, 1.3],
235        [6.8, 3. , 5.5, 2.1],
236        [5.5, 3.5, 1.3, 0.2],
237        [4.8, 3.4, 1.9, 0.2],
238        [5.4, 3.4, 1.7, 0.2],
239        [7.7, 3.8, 6.7, 2.2],
240        [6.9, 3.1, 5.4, 2.1],
241        [5.6, 2.5, 3.9, 1.1],
242        [5.1, 3.4, 1.5, 0.2],
243        [5.1, 3.8, 1.6, 0.2],
244        [6.1, 2.9, 4.7, 1.4],
245        [5.8, 4. , 1.2, 0.2],
246        [5. , 2. , 3.5, 1. ],
247        [6.4, 3.2, 4.5, 1.5],
248        [6.1, 3. , 4.6, 1.4],
249        [5.9, 3.2, 4.8, 1.8],
250        [6. , 2.2, 4. , 1. ],
251        [7.4, 2.8, 6.1, 1.9]])
252 [13]
253 X_train = standardScaler.transform(X_train)
254 [14]
255 X_train
256 array([[-0.63926119,  1.39846731, -1.2315212 , -1.26832688],
257        [-1.01078752,  0.94173615, -1.17475662, -0.73827982],
258        [-1.75384019, -0.42845732, -1.28828579, -1.26832688],
259        [-0.02005063, -0.88518848,  0.13082885,  0.05679076],
260        [-0.7631033 ,  0.71337057, -1.28828579, -1.26832688],
261        [-1.50615597,  0.71337057, -1.28828579, -1.13581511],
262        [ 0.84684415,  0.25663941,  0.81200388,  1.11688486],
263        [-0.14389274, -0.42845732,  0.30112261,  0.18930252],
264        [ 0.97068626, -0.20009175,  0.41465178,  0.32181428],
265        [ 0.2276336 , -0.42845732,  0.47141637,  0.45432605],
266        [-1.38231385,  0.25663941, -1.17475662, -1.26832688],
267        [-1.13462963,  1.17010173, -1.28828579, -1.40083864],
268        [ 1.09452838,  0.02827383,  1.09582681,  1.64693191],
269        [ 0.59915993, -0.88518848,  0.69847471,  0.85186133],
270        [ 0.35147571, -0.6568229 ,  0.58494554,  0.05679076],
271        [ 0.47531782, -0.6568229 ,  0.64171013,  0.85186133],
272        [-0.14389274,  2.99702636, -1.2315212 , -1.00330335],
273        [ 0.59915993, -1.34191964,  0.69847471,  0.45432605],
274        [ 0.72300204, -0.42845732,  0.3578872 ,  0.18930252],
275        [-0.88694541,  1.62683289, -1.00446286, -1.00330335],
276        [ 1.21837049, -0.6568229 ,  0.64171013,  0.32181428],
277        [-0.88694541,  0.94173615, -1.28828579, -1.13581511],
278        [-1.8776823 , -0.20009175, -1.45857955, -1.40083864],
279        [ 0.10379148, -0.20009175,  0.81200388,  0.85186133],
280        [ 0.72300204, -0.6568229 ,  1.09582681,  1.24939662],
281        [-0.26773485, -0.6568229 ,  0.69847471,  1.11688486],
282        [-0.39157696, -1.57028522,  0.01729968, -0.20823277],
283        [ 1.3422126 ,  0.02827383,  0.69847471,  0.45432605],
284        [ 0.59915993,  0.71337057,  1.09582681,  1.64693191],
285        [ 0.84684415, -0.20009175,  1.20935598,  1.38190839],
286        [-0.14389274,  1.62683289, -1.11799203, -1.13581511],
287        [ 0.97068626, -0.42845732,  0.52818096,  0.18930252],
288        [ 1.09452838,  0.48500499,  1.1525914 ,  1.77944368],
289        [-1.25847174, -0.20009175, -1.28828579, -1.40083864],
290        [-1.01078752,  1.17010173, -1.28828579, -1.26832688],
291        [ 0.2276336 , -0.20009175,  0.64171013,  0.85186133],
292        [-1.01078752, -0.20009175, -1.17475662, -1.26832688],
293        [ 0.35147571, -0.20009175,  0.69847471,  0.85186133],
294        [ 0.72300204,  0.02827383,  1.03906223,  0.85186133],
295        [-0.88694541,  1.39846731, -1.2315212 , -1.00330335],
296        [-0.14389274, -0.20009175,  0.30112261,  0.05679076],
297        [-1.01078752,  0.94173615, -1.34505037, -1.13581511],
298        [-0.88694541,  1.62683289, -1.2315212 , -1.13581511],
299        [-1.50615597,  0.25663941, -1.28828579, -1.26832688],
300        [-0.51541907, -0.20009175,  0.47141637,  0.45432605],
301        [ 0.84684415, -0.6568229 ,  0.52818096,  0.45432605],
302        [ 0.35147571, -0.6568229 ,  0.18759344,  0.18930252],
303        [-1.25847174,  0.71337057, -1.17475662, -1.26832688],
304        [-0.88694541,  0.48500499, -1.11799203, -0.87079159],
305        [-0.02005063, -0.88518848,  0.81200388,  0.9843731 ],
306        [-0.26773485, -0.20009175,  0.24435803,  0.18930252],
307        [ 0.59915993, -0.6568229 ,  0.81200388,  0.45432605],
308        [ 1.09452838,  0.48500499,  1.1525914 ,  1.24939662],
309        [ 1.71373893, -0.20009175,  1.20935598,  0.58683781],
310        [ 1.09452838, -0.20009175,  0.86876847,  1.51442015],
311        [-1.13462963,  0.02827383, -1.2315212 , -1.40083864],
312        [-1.13462963, -1.34191964,  0.47141637,  0.71934957],
313        [-0.14389274, -1.34191964,  0.7552393 ,  1.11688486],
314        [-1.13462963, -1.57028522, -0.20975866, -0.20823277],
315        [-0.39157696, -1.57028522,  0.07406427, -0.07572101],
316        [ 1.09452838, -1.34191964,  1.20935598,  0.85186133],
317        [ 0.84684415, -0.20009175,  1.03906223,  0.85186133],
318        [-0.14389274, -1.11355406, -0.09622949, -0.20823277],
319        [ 0.2276336 , -2.02701638,  0.7552393 ,  0.45432605],
320        [ 1.09452838,  0.02827383,  0.58494554,  0.45432605],
321        [-1.13462963,  0.02827383, -1.2315212 , -1.26832688],
322        [ 0.59915993, -1.34191964,  0.7552393 ,  0.9843731 ],
323        [-1.38231385,  0.25663941, -1.34505037, -1.26832688],
324        [ 0.2276336 , -0.88518848,  0.81200388,  0.58683781],
325        [-0.02005063, -1.11355406,  0.18759344,  0.05679076],
326        [ 1.3422126 ,  0.25663941,  1.1525914 ,  1.51442015],
327        [-1.75384019, -0.20009175, -1.34505037, -1.26832688],
328        [ 1.58989682, -0.20009175,  1.26612057,  1.24939662],
329        [ 1.21837049,  0.25663941,  1.26612057,  1.51442015],
330        [-0.7631033 ,  0.94173615, -1.2315212 , -1.26832688],
331        [ 2.58063371,  1.62683289,  1.5499435 ,  1.11688486],
332        [ 0.72300204, -0.6568229 ,  1.09582681,  1.38190839],
333        [-0.26773485, -0.42845732, -0.0394649 ,  0.18930252],
334        [-0.39157696,  2.5402952 , -1.28828579, -1.26832688],
335        [-1.25847174, -0.20009175, -1.28828579, -1.13581511],
336        [ 0.59915993, -0.42845732,  1.09582681,  0.85186133],
337        [-1.75384019,  0.25663941, -1.34505037, -1.26832688],
338        [-0.51541907,  1.85519847, -1.11799203, -1.00330335],
339        [-1.01078752,  0.71337057, -1.17475662, -1.00330335],
340        [ 1.09452838, -0.20009175,  0.7552393 ,  0.71934957],
341        [-0.51541907,  1.85519847, -1.34505037, -1.00330335],
342        [ 2.33294949, -0.6568229 ,  1.72023726,  1.11688486],
343        [-0.26773485, -0.88518848,  0.30112261,  0.18930252],
344        [ 1.21837049, -0.20009175,  1.03906223,  1.24939662],
345        [-0.39157696,  0.94173615, -1.34505037, -1.26832688],
346        [-1.25847174,  0.71337057, -1.00446286, -1.26832688],
347        [-0.51541907,  0.71337057, -1.11799203, -1.26832688],
348        [ 2.33294949,  1.62683289,  1.72023726,  1.38190839],
349        [ 1.3422126 ,  0.02827383,  0.98229764,  1.24939662],
350        [-0.26773485, -1.34191964,  0.13082885, -0.07572101],
351        [-0.88694541,  0.71337057, -1.2315212 , -1.26832688],
352        [-0.88694541,  1.62683289, -1.17475662, -1.26832688],
353        [ 0.35147571, -0.42845732,  0.58494554,  0.32181428],
354        [-0.02005063,  2.08356405, -1.40181496, -1.26832688],
355        [-1.01078752, -2.48374754, -0.09622949, -0.20823277],
356        [ 0.72300204,  0.25663941,  0.47141637,  0.45432605],
357        [ 0.35147571, -0.20009175,  0.52818096,  0.32181428],
358        [ 0.10379148,  0.25663941,  0.64171013,  0.85186133],
359        [ 0.2276336 , -2.02701638,  0.18759344, -0.20823277],
360        [ 1.96142316, -0.6568229 ,  1.37964974,  0.9843731 ]])
361 [15]
362 X_test_standard = standardScaler.transform(X_test)
363 [16]
364 X_test_standard
365 array([[-0.26773485, -0.20009175,  0.47141637,  0.45432605],
366        [-0.02005063, -0.6568229 ,  0.81200388,  1.64693191],
367        [-1.01078752, -1.7986508 , -0.20975866, -0.20823277],
368        [-0.02005063, -0.88518848,  0.81200388,  0.9843731 ],
369        [-1.50615597,  0.02827383, -1.2315212 , -1.26832688],
370        [-0.39157696, -1.34191964,  0.18759344,  0.18930252],
371        [-0.14389274, -0.6568229 ,  0.47141637,  0.18930252],
372        [ 0.84684415, -0.20009175,  0.86876847,  1.11688486],
373        [ 0.59915993, -1.7986508 ,  0.41465178,  0.18930252],
374        [-0.39157696, -1.11355406,  0.41465178,  0.05679076],
375        [ 1.09452838,  0.02827383,  0.41465178,  0.32181428],
376        [-1.62999808, -1.7986508 , -1.34505037, -1.13581511],
377        [-1.25847174,  0.02827383, -1.17475662, -1.26832688],
378        [-0.51541907,  0.71337057, -1.2315212 , -1.00330335],
379        [ 1.71373893,  1.17010173,  1.37964974,  1.77944368],
380        [-0.02005063, -0.88518848,  0.24435803, -0.20823277],
381        [-1.50615597,  1.17010173, -1.51534413, -1.26832688],
382        [ 1.71373893,  0.25663941,  1.32288516,  0.85186133],
383        [ 1.3422126 ,  0.02827383,  0.81200388,  1.51442015],
384        [ 0.72300204, -0.88518848,  0.92553306,  0.9843731 ],
385        [ 0.59915993,  0.48500499,  0.58494554,  0.58683781],
386        [-1.01078752,  0.71337057, -1.2315212 , -1.26832688],
387        [ 2.33294949, -1.11355406,  1.83376643,  1.51442015],
388        [-1.01078752,  0.48500499, -1.28828579, -1.26832688],
389        [ 0.47531782, -0.42845732,  0.3578872 ,  0.18930252],
390        [ 0.10379148, -0.20009175,  0.30112261,  0.45432605],
391        [-1.01078752,  0.25663941, -1.40181496, -1.26832688],
392        [-0.39157696, -1.7986508 ,  0.18759344,  0.18930252],
393        [ 0.59915993,  0.48500499,  1.32288516,  1.77944368],
394        [ 2.33294949, -0.20009175,  1.37964974,  1.51442015],
395        [-0.88694541,  0.94173615, -1.28828579, -1.26832688],
396        [-1.13462963, -0.20009175, -1.28828579, -1.26832688],
397        [-0.14389274, -0.6568229 ,  0.24435803,  0.18930252],
398        [ 0.47531782,  0.71337057,  0.98229764,  1.51442015],
399        [-0.88694541, -1.34191964, -0.38005242, -0.07572101],
400        [ 1.46605471,  0.25663941,  0.58494554,  0.32181428],
401        [ 0.35147571, -1.11355406,  1.09582681,  0.32181428],
402        [ 2.20910738, -0.20009175,  1.66347267,  1.24939662],
403        [-0.7631033 ,  2.31192962, -1.2315212 , -1.40083864],
404        [ 0.47531782, -2.02701638,  0.47141637,  0.45432605],
405        [ 1.83758104, -0.42845732,  1.49317891,  0.85186133],
406        [ 0.72300204,  0.25663941,  0.92553306,  1.51442015],
407        [ 0.2276336 ,  0.71337057,  0.47141637,  0.58683781],
408        [-0.7631033 , -0.88518848,  0.13082885,  0.32181428],
409        [-0.51541907,  1.39846731, -1.2315212 , -1.26832688]])
410 [17]
411 from sklearn.neighbors import KNeighborsClassifier
412 [18]
413 knn_clf = KNeighborsClassifier(n_neighbors=3)
414 [19]
415 knn_clf.fit(X_train,y_train)
416 KNeighborsClassifier(n_neighbors=3)
417 [20]
418 knn_clf.score(X_test_standard,y_test)
419 0.9777777777777777
420 [21]
421 knn_clf.score(X_test,y_test)
422 0.3333333333333333