sklearn 笔记二
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#导入波士顿数据集
from sklearn.datasets import load_boston
boston = load_boston()
#转化为数据框
import pandas as pd
boston_df = pd.DataFrame(boston.data,columns=boston.feature_names)
boston_df
| CRIM | ZN | INDUS | CHAS | NOX | RM | AGE | DIS | RAD | TAX | PTRATIO | B | LSTAT | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.00632 | 18.0 | 2.31 | 0.0 | 0.538 | 6.575 | 65.2 | 4.0900 | 1.0 | 296.0 | 15.3 | 396.90 | 4.98 |
| 1 | 0.02731 | 0.0 | 7.07 | 0.0 | 0.469 | 6.421 | 78.9 | 4.9671 | 2.0 | 242.0 | 17.8 | 396.90 | 9.14 |
| 2 | 0.02729 | 0.0 | 7.07 | 0.0 | 0.469 | 7.185 | 61.1 | 4.9671 | 2.0 | 242.0 | 17.8 | 392.83 | 4.03 |
| 3 | 0.03237 | 0.0 | 2.18 | 0.0 | 0.458 | 6.998 | 45.8 | 6.0622 | 3.0 | 222.0 | 18.7 | 394.63 | 2.94 |
| 4 | 0.06905 | 0.0 | 2.18 | 0.0 | 0.458 | 7.147 | 54.2 | 6.0622 | 3.0 | 222.0 | 18.7 | 396.90 | 5.33 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 501 | 0.06263 | 0.0 | 11.93 | 0.0 | 0.573 | 6.593 | 69.1 | 2.4786 | 1.0 | 273.0 | 21.0 | 391.99 | 9.67 |
| 502 | 0.04527 | 0.0 | 11.93 | 0.0 | 0.573 | 6.120 | 76.7 | 2.2875 | 1.0 | 273.0 | 21.0 | 396.90 | 9.08 |
| 503 | 0.06076 | 0.0 | 11.93 | 0.0 | 0.573 | 6.976 | 91.0 | 2.1675 | 1.0 | 273.0 | 21.0 | 396.90 | 5.64 |
| 504 | 0.10959 | 0.0 | 11.93 | 0.0 | 0.573 | 6.794 | 89.3 | 2.3889 | 1.0 | 273.0 | 21.0 | 393.45 | 6.48 |
| 505 | 0.04741 | 0.0 | 11.93 | 0.0 | 0.573 | 6.030 | 80.8 | 2.5050 | 1.0 | 273.0 | 21.0 | 396.90 | 7.88 |
506 rows × 13 columns
from sklearn.preprocessing import StandardScaler
std = StandardScaler(with_mean=False)#实例化
std.fit(boston.data)#训练
std.transform(boston.data)#转化或预测predit
array([[7.35478830e-04, 7.72552226e-01, 3.37050589e-01, ...,
7.07414614e+00, 4.35175383e+00, 6.98065441e-01],
[3.17815298e-03, 0.00000000e+00, 1.03157907e+00, ...,
8.23005237e+00, 4.35175383e+00, 1.28118838e+00],
[3.17582552e-03, 0.00000000e+00, 1.03157907e+00, ...,
8.23005237e+00, 4.30712889e+00, 5.64900347e-01],
...,
[7.07083761e-03, 0.00000000e+00, 1.74069850e+00, ...,
9.70961235e+00, 4.35175383e+00, 7.90580138e-01],
[1.27533426e-02, 0.00000000e+00, 1.74069850e+00, ...,
9.70961235e+00, 4.31392680e+00, 9.08326116e-01],
[5.51725496e-03, 0.00000000e+00, 1.74069850e+00, ...,
9.70961235e+00, 4.35175383e+00, 1.10456941e+00]])
from sklearn.linear_model import Ridge#岭回归
ridge = Ridge()
ridge.fit(boston.data,boston.target)#只要fit没有报错 决策树就在那里了
Ridge()
ridge.predict(boston.data)
array([30.25311604, 24.80547336, 30.53232402, 28.91100981, 28.1832052 ,
25.43673125, 22.95817615, 19.30237669, 11.15984468, 18.82155521,
18.81422924, 21.50541027, 20.98474447, 20.02619643, 19.57611906,
19.77429479, 21.18939422, 17.1605386 , 16.62180745, 18.70110592,
12.55036954, 17.85752738, 15.96917368, 13.87790039, 15.85205327,
13.56864661, 15.69127486, 14.88600692, 19.7779414 , 21.14109205,
11.53723803, 18.20402304, 8.86340284, 14.34902491, 13.72058572,
23.74902824, 22.29387549, 23.26962048, 23.12831217, 31.50726633,
34.44042267, 28.24553988, 25.37070495, 24.76917355, 22.86995627,
21.97930271, 20.27968185, 17.60436025, 8.50373809, 16.9632888 ,
21.14154831, 23.76708853, 27.76078883, 24.10242763, 15.36344139,
31.50946541, 25.14328079, 33.13255864, 22.15413588, 21.23511061,
17.89448419, 18.31780092, 24.08675693, 22.8694662 , 23.62325842,
30.19359472, 25.21146597, 21.00266342, 17.16191352, 20.5981506 ,
25.00985187, 21.43498919, 24.35585884, 23.8371262 , 25.2080971 ,
23.43556655, 22.15782911, 22.76179825, 20.69957277, 21.93315055,
28.38175107, 26.72372007, 25.98863394, 24.90805019, 24.69924957,
27.65606798, 22.02003456, 25.64526212, 30.53861313, 30.83718179,
27.01627565, 27.2674471 , 28.49279555, 28.79043764, 26.41268031,
28.34921822, 24.33157153, 35.49381798, 35.05869611, 31.99969554,
24.56019734, 25.65227414, 19.58856476, 20.17891334, 21.26592833,
18.24878212, 16.91768243, 20.56146371, 22.45220726, 19.58357147,
20.73054631, 26.34324536, 20.40076435, 20.34915937, 24.90509154,
20.08437034, 23.21495764, 23.4768466 , 20.08205861, 20.64337013,
21.76980356, 22.2330481 , 20.22115088, 15.91921622, 20.19862996,
22.19401005, 14.12916481, 15.1895029 , 18.98479636, 14.08563895,
20.136786 , 19.53534754, 20.19744137, 15.86586401, 13.26739836,
17.32551279, 15.9314865 , 19.42098652, 13.77154689, 16.44765748,
13.53870147, 3.76808991, 15.61521208, 13.32165028, 9.85363597,
13.13994749, 17.00643157, 9.66072513, 10.89203921, 16.04762814,
22.16670316, 19.57344947, 21.32812003, 18.53621121, 23.55324461,
21.29729493, 14.82663415, 32.8678 , 28.55237379, 26.9652496 ,
32.1560116 , 36.48602753, 40.11560604, 41.45574764, 24.393251 ,
24.9347885 , 36.89209418, 22.70405639, 25.93783279, 26.22533245,
22.07928659, 23.83944713, 22.57347501, 28.78596821, 26.30515476,
30.83343626, 25.62200707, 29.00491136, 31.27284372, 32.94788467,
34.60141408, 27.66938295, 33.70254012, 30.76887605, 22.4288443 ,
24.66526613, 36.01902606, 32.94221498, 32.08719489, 34.15603239,
30.69040488, 30.16029803, 32.85296581, 32.03509304, 31.41772152,
40.745516 , 35.84547294, 32.30950167, 34.37272783, 30.23270182,
30.79514169, 29.00673506, 37.03118034, 41.81114966, 42.99177036,
22.71864333, 23.58678606, 17.5483317 , 23.16321498, 16.2900651 ,
21.80009712, 16.38682337, 22.36050358, 25.21680994, 10.98863632,
24.43993099, 26.32661314, 28.13303501, 24.35138154, 29.26117901,
32.69974293, 23.15802921, 31.7596205 , 29.49113764, 38.16890048,
39.5994642 , 37.36669104, 32.14860606, 35.65184875, 31.35528002,
24.25764503, 33.12483431, 38.01330283, 37.10697328, 31.43828861,
25.10942516, 29.8080276 , 32.66599059, 28.3399894 , 28.19595613,
26.98265295, 23.32260659, 23.86035931, 27.4718839 , 16.11880291,
13.18294303, 20.12926592, 19.69533559, 21.2959682 , 24.32548108,
24.43139034, 25.303225 , 25.37516241, 30.45365152, 23.99396196,
21.76380283, 37.30093785, 43.76945831, 36.79474527, 35.30203623,
35.23859984, 37.58160852, 41.45680553, 34.78426292, 36.19306614,
28.69604563, 31.58296184, 40.94890797, 39.56376099, 25.33449757,
22.23722091, 27.36972148, 28.36894129, 35.3476283 , 35.85691003,
33.64970133, 35.33431489, 34.6999873 , 30.20460155, 35.03322494,
38.43465107, 34.14868517, 39.98107783, 44.36867851, 31.54167324,
27.10211157, 20.23211168, 26.83504642, 26.92005262, 26.74451041,
33.42417844, 34.404105 , 31.8274714 , 25.39258966, 23.83779454,
28.04625701, 26.81272788, 18.87932807, 28.95515596, 31.84815122,
30.46881837, 28.56758613, 28.63982495, 32.61775495, 33.35272302,
30.6964216 , 35.43618244, 32.56694035, 28.70066141, 23.58456381,
18.63935837, 27.01480803, 23.15189993, 25.54939617, 25.49486313,
20.5894211 , 17.5867192 , 18.43712886, 24.40242985, 21.50502313,
25.02558249, 24.99659338, 23.02087979, 19.40189243, 25.35907181,
25.14368316, 24.05696753, 19.55939823, 21.10745209, 24.27877016,
21.56926634, 19.9434429 , 23.37129441, 22.86910087, 22.27154189,
21.29076426, 20.66053013, 19.73660482, 22.6973132 , 21.71730727,
21.82017921, 30.39373572, 22.34617074, 27.85225837, 28.96871545,
16.67740255, 14.8617906 , 25.5660512 , 27.88190418, 22.68143111,
20.91411263, 20.93159193, 17.32978295, 25.98008645, 15.08098969,
17.3967763 , 20.1880358 , 23.38018884, 22.94274308, 20.05543074,
23.52575704, 19.67718717, 18.9282955 , 20.7935798 , 37.93998642,
14.55892566, 15.79871882, 10.32600529, 23.47089448, 32.27606665,
34.24900021, 24.60924536, 25.83008793, 5.79442553, 0.40594821,
25.34203597, 17.66610116, 20.14314857, 15.77040843, 16.73759076,
14.64117815, 18.41304471, 13.47838905, 13.09458531, 3.25429082,
8.05937042, 6.14023357, 5.6772214 , 6.42332067, 14.29389936,
17.37193075, 17.56117984, 9.94794879, 20.38941973, 18.08870036,
20.42202882, 19.39506073, 16.38463401, 6.58001565, 10.95979707,
11.93096904, 17.89389942, 18.32950752, 13.09625823, 7.52530609,
8.40512199, 7.75926124, 19.80435383, 12.98193064, 19.20179053,
14.6629195 , 16.2417319 , 0.8138044 , 11.15016645, -4.41798623,
9.40343956, 13.25742201, 6.79644722, 6.20099973, 14.89582695,
19.89571627, 18.41312853, 18.14343541, 12.6390845 , 14.00836516,
9.78080721, 15.8318979 , 14.20617052, 14.26387595, 12.92513874,
17.50424713, 18.00221548, 21.00598604, 17.30603415, 16.21010351,
13.73471604, 14.9082218 , 9.06743846, 5.12132491, 13.46871729,
13.17942264, 17.76415447, 19.19231116, 18.51190751, 11.8664457 ,
12.29258533, 18.1332901 , 18.6340791 , 17.8140489 , 17.4781204 ,
16.74054381, 19.71714924, 18.92453713, 22.85510384, 15.50644244,
16.08329217, 12.90931391, 13.19079201, 17.59861521, 18.95864379,
19.40280127, 20.57920551, 20.2465411 , 22.90310715, 20.52312578,
18.1395711 , 14.25695016, 16.32023868, 16.58559074, 18.28969752,
19.70046697, 22.17128747, 22.0544957 , 25.46642115, 15.76906728,
15.41138724, 20.16718754, 11.04366756, 18.80926036, 21.55711785,
22.88063513, 26.48530568, 27.97254175, 20.68406891, 19.33363789,
22.12126818, 19.30052115, 21.10843893, 11.3536993 , 7.60994365,
2.9864527 , 13.24586045, 15.57927321, 20.92845662, 20.98041181,
17.33314245, 14.1505575 , 19.34781614, 21.53988395, 18.60660466,
20.61997096, 23.9459692 , 22.70817479, 27.92757 , 26.44495268,
22.68035106])
import joblib
joblib.dump(ridge,r'C:\Users\enen\笔记2.pkl') #r'中间放路径'用于保存波士顿路径模型 不用fit 可以直接存下来 再用可以直接导进来
['C:\\Users\\enen\\笔记2.pkl']
**在另外一个jupyter 里面直接导入 其可以直接导入引用
!pip install joblib#安装joblib
import joblib
std2 = joblib.load()
from sklearn.datasets import load_boston
boston = boston()
std2.predict(boston.data)**
##对数据进行预处理
对单个数据集进行标准化
对单个数据集进行标准化
sklearn.preprocessing.scale()
X : {array-like,sparse matrix}，需要精心变换数据的数据阵
axis=0以列为标准化 axis=1是以行为标准化
with_mean = True : 是否中心化数据（移除均值）
with_std = True : 是否均一化标准差（除以标准差）
copy = True : 是否生成副本而不是替换原数据*
boston_df.describe()
| CRIM | ZN | INDUS | CHAS | NOX | RM | AGE | DIS | RAD | TAX | PTRATIO | B | LSTAT | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 | 506.000000 |
| mean | 3.613524 | 11.363636 | 11.136779 | 0.069170 | 0.554695 | 6.284634 | 68.574901 | 3.795043 | 9.549407 | 408.237154 | 18.455534 | 356.674032 | 12.653063 |
| std | 8.601545 | 23.322453 | 6.860353 | 0.253994 | 0.115878 | 0.702617 | 28.148861 | 2.105710 | 8.707259 | 168.537116 | 2.164946 | 91.294864 | 7.141062 |
| min | 0.006320 | 0.000000 | 0.460000 | 0.000000 | 0.385000 | 3.561000 | 2.900000 | 1.129600 | 1.000000 | 187.000000 | 12.600000 | 0.320000 | 1.730000 |
| 25% | 0.082045 | 0.000000 | 5.190000 | 0.000000 | 0.449000 | 5.885500 | 45.025000 | 2.100175 | 4.000000 | 279.000000 | 17.400000 | 375.377500 | 6.950000 |
| 50% | 0.256510 | 0.000000 | 9.690000 | 0.000000 | 0.538000 | 6.208500 | 77.500000 | 3.207450 | 5.000000 | 330.000000 | 19.050000 | 391.440000 | 11.360000 |
| 75% | 3.677083 | 12.500000 | 18.100000 | 0.000000 | 0.624000 | 6.623500 | 94.075000 | 5.188425 | 24.000000 | 666.000000 | 20.200000 | 396.225000 | 16.955000 |
| max | 88.976200 | 100.000000 | 27.740000 | 1.000000 | 0.871000 | 8.780000 | 100.000000 | 12.126500 | 24.000000 | 711.000000 | 22.000000 | 396.900000 | 37.970000 |
from sklearn import preprocessing
boston_scaled = preprocessing.scale(boston_df)
boston_scaled
array([[-0.41978194, 0.28482986, -1.2879095 , ..., -1.45900038,
0.44105193, -1.0755623 ],
[-0.41733926, -0.48772236, -0.59338101, ..., -0.30309415,
0.44105193, -0.49243937],
[-0.41734159, -0.48772236, -0.59338101, ..., -0.30309415,
0.39642699, -1.2087274 ],
...,
[-0.41344658, -0.48772236, 0.11573841, ..., 1.17646583,
0.44105193, -0.98304761],
[-0.40776407, -0.48772236, 0.11573841, ..., 1.17646583,
0.4032249 , -0.86530163],
[-0.41500016, -0.48772236, 0.11573841, ..., 1.17646583,
0.44105193, -0.66905833]])
boston_scaled.mean(axis = 0)
array([-8.78743718e-17, -6.34319123e-16, -2.68291099e-15, 4.70199198e-16,
2.49032240e-15, -1.14523016e-14, -1.40785495e-15, 9.21090169e-16,
5.44140929e-16, -8.86861950e-16, -9.20563581e-15, 8.16310129e-15,
-3.37016317e-16])
boston_scaled.mean(axis = 1)
array([-0.39605902, -0.2771074 , -0.30060388, -0.36357242, -0.29625273,
-0.36650822, -0.24657683, -0.06002722, 0.00433699, -0.11334879,
-0.01695502, -0.16754224, -0.30844718, -0.17014375, -0.09351894,
-0.20371953, -0.29101222, -0.06571769, -0.37889648, -0.17831727,
-0.01963552, -0.06049769, 0.02426183, 0.02356969, -0.01018486,
-0.14036983, -0.05390239, -0.07100734, 0.01230097, -0.00868195,
0.00312581, -0.03062388, -0.06307879, -0.05884851, -0.0789437 ,
-0.31175654, -0.33715595, -0.38257187, -0.39136022, -0.2460804 ,
-0.2406656 , -0.48181102, -0.52868858, -0.49727042, -0.4030483 ,
-0.47122192, -0.41855728, -0.18049358, -0.08300532, -0.30172692,
-0.28873818, -0.26985084, -0.38325301, -0.40472035, 0.12966882,
-0.04314863, -0.09647872, -0.11156145, -0.17791078, -0.15460982,
-0.07101349, 0.01291534, -0.05634106, -0.03302886, -0.13043466,
-0.27054991, -0.2291618 , -0.35540097, -0.28931645, -0.31425188,
-0.39923706, -0.39041618, -0.44138308, -0.41491261, -0.37236521,
-0.24150131, -0.15338581, -0.25296746, -0.16592857, -0.29718415,
-0.28595171, -0.1770795 , -0.32070468, -0.29260667, -0.3728418 ,
-0.37390615, -0.40046006, -0.42988176, -0.30313994, -0.35755874,
-0.40347618, -0.38895144, -0.18034398, -0.2932558 , -0.11072237,
-0.46533417, -0.40462906, -0.25079743, -0.39513231, -0.33840543,
-0.06632048, -0.09954952, -0.34833852, -0.06910475, -0.08355922,
-0.06562315, -0.05126485, -0.09730686, -0.02865859, -0.03434536,
-0.15720936, -0.10366915, -0.1076047 , -0.06201572, -0.16475146,
-0.16143238, -0.16709163, -0.17338503, -0.21821449, -0.2192348 ,
-0.1069755 , -0.06584218, -0.01051381, 0.05858965, -0.01715133,
-0.05063705, 0.03405929, 0.15551733, 0.23016281, 0.17483933,
0.21306349, 0.20642912, 0.18699496, 0.16739478, 0.08810655,
0.24603306, 0.16824899, 0.22218944, 0.21952023, 0.22682148,
0.26725426, 0.28181347, 0.45638151, 0.16628625, 0.11509134,
0.04968557, -0.1270667 , 0.11059927, 0.09374386, 0.06684715,
0.06309459, -0.04576109, 0.16788782, -0.04955323, 0.33341744,
0.12052707, -0.24535688, -0.12825252, -0.20877667, 0.03682421,
0.07252725, -0.10246226, 0.27595477, 0.34272226, -0.13113899,
-0.2501731 , -0.01491909, -0.30277988, -0.16816653, -0.12409282,
-0.18693656, -0.14924037, -0.36787531, -0.34810873, -0.44475278,
-0.49965509, -0.44978886, -0.39380641, -0.34280209, -0.44670255,
-0.26966649, -0.48872459, -0.34418154, -0.38270572, -0.41414766,
-0.41318985, -0.36301424, -0.32076924, -0.38164971, -0.26547979,
-0.28803891, -0.28030002, -0.26330937, -0.39600255, -0.41427203,
-0.22864465, -0.26012065, -0.26002524, -0.2266545 , -0.08106303,
-0.07929427, -0.27155081, -0.22029642, -0.1721172 , -0.16788062,
-0.39281747, -0.24736106, -0.17955221, 0.0684229 , 0.19480188,
0.17260385, 0.17017427, 0.02792435, -0.33780486, -0.33025955,
-0.32324709, 0.00362769, -0.16698236, 0.15737583, 0.13448534,
0.12213637, 0.18048674, 0.1033541 , -0.23963456, -0.13165714,
-0.06389395, -0.13057204, -0.22254124, -0.35420785, -0.46449901,
-0.30424276, -0.19488431, -0.11599538, -0.13270539, 0.02253197,
-0.32101404, 0.0981591 , -0.18930307, -0.36829797, -0.2751033 ,
-0.15504367, -0.19961515, -0.2097883 , -0.39246565, -0.08345766,
-0.02120916, -0.15703108, -0.02536924, -0.10168169, -0.1725189 ,
-0.23128554, -0.28831986, -0.17619149, -0.0173379 , -0.16590802,
-0.19590472, -0.15793103, -0.14961807, -0.2367595 , -0.2886932 ,
-0.26651272, -0.2388842 , -0.14743826, -0.19842677, -0.26614999,
-0.49221502, -0.22790028, -0.25343912, -0.40861999, 0.01976088,
-0.33218069, -0.42405767, -0.28389125, 0.12818912, -0.0295187 ,
-0.29371018, 0.11740238, -0.00392561, -0.32314122, -0.5200843 ,
-0.32094773, -0.45577259, -0.07503936, 0.01832809, -0.166123 ,
-0.32532459, -0.12917749, -0.23892079, -0.18316336, -0.21852889,
-0.1548671 , -0.12108966, -0.17770934, -0.43001869, -0.35795505,
-0.34264851, -0.29436655, -0.24993689, -0.30660206, -0.25769987,
-0.14352556, -0.24090334, -0.33016947, -0.31397828, -0.23957017,
-0.26356265, -0.17096471, -0.22366405, -0.20060816, -0.24028555,
-0.4668612 , -0.34464322, -0.18890434, -0.21095859, -0.13553499,
-0.22021159, -0.11090252, -0.17681054, -0.20180929, -0.21002934,
-0.25405248, -0.2574941 , -0.28916738, -0.22082263, -0.29211472,
-0.34970009, -0.30864583, -0.2222199 , -0.45420933, -0.46122159,
-0.40373081, -0.32812948, -0.38299824, -0.27571839, -0.26412623,
-0.30235393, -0.29681252, -0.2371465 , -0.33048868, -0.30234158,
-0.27316411, -0.2699344 , -0.29997688, -0.06917567, -0.13546462,
-0.24129709, -0.23800406, -0.01777884, -0.11252826, -0.10151959,
-0.11643139, -0.01647809, -0.09732668, 0.01484115, 0.04394562,
0.03542527, 0.90236861, 0.83776012, 0.79180642, 0.47391082,
0.46015991, 0.48691762, 0.3772965 , 0.73125982, 0.90002655,
0.05748605, 0.25967112, 0.02102608, 0.1559248 , 0.6507523 ,
0.6970944 , 0.38499971, 0.61441981, 0.58108994, 0.59622412,
0.69242249, 0.64653125, 0.63603803, 0.73475678, 0.65293121,
1.30347558, 0.66790679, 0.5486094 , 0.54478439, 0.47106464,
0.65538418, 0.63418366, 0.66939067, 0.57859249, 0.49608159,
0.48047732, 0.47599141, 0.53637208, 0.50767267, 0.53448154,
0.57022389, 0.55648898, 0.50956348, 0.86798909, 0.54061001,
0.7698378 , 0.64199831, 0.59187339, 0.61548083, 0.77785891,
1.06342491, 0.43135004, 0.36486682, 0.4235531 , 0.44252794,
0.39576702, 0.31376962, 0.27150325, 0.41825289, 0.64658872,
0.46901226, 0.37658162, 0.43728822, 0.81193041, 0.36559001,
0.52522896, 0.4405452 , 0.3177017 , 0.1738894 , 0.00730401,
0.31354525, 0.03767671, 0.39782483, 0.26785161, 0.35228999,
0.20491102, 0.3161625 , 0.12124508, 0.28756642, 0.34221761,
0.47210738, 0.34911335, 0.41236533, 0.47056046, 0.57119361,
0.6902484 , 0.63218479, 0.55743457, 0.64092582, 0.51478275,
0.39597227, 0.51640238, 0.58916204, 0.59201533, 0.53195162,
0.2741078 , 0.57136564, 0.53213324, 0.69062653, 0.32630375,
0.26949978, 0.19309309, 0.17859093, 0.44299814, 0.49855854,
0.45868858, 0.50335411, 0.51224511, 0.50509928, 0.42439941,
0.24704782, 0.13547399, 0.42018917, 0.45530289, 0.35477485,
0.42525489, 0.37991207, 0.39444806, 0.40778515, 0.37168107,
0.4568779 , 0.51208983, 0.50405556, 0.50993568, 0.46965163,
0.31643769, 0.36001296, 0.39692306, 0.1880272 , 0.23338375,
0.31577792, 0.42302933, 0.26348245, 0.27416626, 0.30367439,
0.31030901, 0.34605471, 0.26792737, -0.2260277 , -0.21522384,
-0.22648972, -0.094337 , -0.12901299, -0.14952233, -0.15473899,
-0.09092583, -0.1313857 , -0.17179601, -0.08021259, -0.09012741,
-0.17541044])
boston_scaled.std(axis = 0)
array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])
boston_scaled.std(axis = 1)
array([0.62434741, 0.49053373, 0.66078337, 0.77724728, 0.77114357,
0.65241767, 0.53119222, 0.70278112, 0.98739399, 0.68190377,
0.73762676, 0.62014196, 0.59111883, 0.51661341, 0.50797092,
0.51832343, 0.60823053, 0.50840367, 0.56656305, 0.49511787,
0.69665747, 0.53134138, 0.58102466, 0.6611238 , 0.5810275 ,
0.59363797, 0.56314904, 0.56578475, 0.55007365, 0.52910449,
0.69260526, 0.56556068, 0.85310111, 0.61559551, 0.69668107,
0.35782901, 0.32483392, 0.40831045, 0.44899715, 1.11184827,
1.19767335, 0.80737382, 0.70796358, 0.71427851, 0.52581237,
0.51850994, 0.543583 , 0.63204606, 1.01760106, 0.61146415,
0.68243802, 0.65627243, 0.80302176, 0.75816353, 1.11967739,
1.47814501, 1.37061842, 1.50648023, 0.83502713, 0.69100184,
0.70216808, 0.69010507, 0.71211739, 0.81406701, 1.04269756,
1.22040787, 1.16059605, 0.71912014, 0.67751077, 0.66456911,
0.73305276, 0.62691212, 0.72121312, 0.71386441, 0.67459402,
0.4186009 , 0.35748256, 0.38961022, 0.41507276, 0.4723302 ,
0.70372997, 0.61230749, 0.65113679, 0.58622002, 0.48731544,
0.50733078, 0.46236376, 0.40145763, 0.62566577, 0.58322025,
0.39971963, 0.42614703, 0.50216535, 0.600219 , 0.48157957,
0.44142041, 0.42461512, 0.94329536, 0.88010624, 0.70351022,
0.51091215, 0.52113564, 0.9302167 , 0.49222496, 0.50996957,
0.60149058, 0.60743304, 0.50722185, 0.55272627, 0.52380131,
0.44976148, 0.37479918, 0.44369965, 0.45112758, 0.33931588,
0.37168839, 0.2618744 , 0.31770705, 0.2928778 , 0.30417747,
0.80821864, 0.81620378, 0.86779918, 0.99643471, 0.88133853,
0.83796443, 1.05603315, 0.79147698, 0.73720367, 0.79282068,
0.72163172, 0.70725398, 0.71174461, 0.73043179, 0.82382019,
0.72389842, 0.74145817, 0.73175623, 0.82436899, 0.76660982,
0.83268649, 1.22314409, 1.55930197, 1.24137995, 1.36903834,
1.35062337, 1.24767863, 1.36154697, 1.2928887 , 1.14558557,
1.06791913, 1.12911567, 1.53311248, 1.13426463, 1.4359861 ,
1.65948061, 1.37431644, 0.8419895 , 0.7774489 , 1.09073231,
1.28222814, 0.95825574, 1.43087972, 1.50228132, 0.72284314,
0.76617038, 1.04066878, 0.73758479, 0.73631027, 0.71800199,
0.73972513, 0.72752238, 0.52052034, 0.45172922, 0.34623083,
0.47503733, 0.34474634, 0.41141689, 0.50432976, 0.60596958,
0.8516778 , 0.43114036, 0.73673329, 0.63469503, 0.57595069,
0.45597469, 0.86348688, 0.7918765 , 0.82700895, 0.8932783 ,
0.98418836, 0.9353915 , 1.02994 , 1.13415494, 1.07656906,
1.42379737, 1.46720242, 1.4085283 , 1.44191868, 1.445892 ,
1.46252595, 1.20883321, 1.42807953, 1.49324617, 1.53588018,
0.48846997, 0.3564085 , 0.45573527, 1.09384406, 1.24241673,
1.12010431, 1.22764939, 1.11966368, 0.42405688, 0.95804409,
0.36325123, 1.13068702, 0.48371652, 1.15862706, 1.13050062,
1.13958464, 1.15166337, 1.11506258, 0.40951007, 0.93512475,
1.09565203, 0.88602149, 0.53779166, 0.83722219, 0.52667893,
0.25513595, 0.62560999, 0.97272515, 0.91659392, 1.10827158,
0.23151509, 1.09832882, 0.6227914 , 0.77652029, 0.69359359,
0.71007712, 0.64558698, 0.70211338, 0.89489844, 0.75781929,
0.79425923, 0.79451244, 0.73263437, 0.72661056, 0.89558222,
0.87180461, 0.90425882, 1.09405211, 1.32340317, 1.33604323,
1.36623101, 1.38517807, 1.34712283, 1.02077341, 0.95048762,
0.937667 , 1.02488076, 1.25279853, 0.99329669, 0.96958016,
0.80227971, 0.91789383, 1.15265015, 0.95824532, 1.14438507,
0.49367571, 0.63667084, 0.48087642, 1.28936789, 1.29447473,
0.73150266, 1.28149562, 1.31376342, 0.66272373, 0.73441097,
0.96918056, 0.79861387, 1.43418076, 1.90657327, 1.40683934,
1.06425141, 1.32445136, 0.95069333, 0.91399956, 0.97385537,
1.1601901 , 1.18942591, 1.14324066, 0.67291857, 0.56785164,
0.70661594, 0.62308263, 0.65234898, 1.2365059 , 1.32543227,
1.20685857, 0.66797875, 0.74589295, 0.84514639, 0.75964138,
0.59290498, 0.7815145 , 0.64639868, 0.46298829, 0.31105397,
0.51031786, 0.33365011, 0.3910866 , 0.35558015, 0.3988559 ,
0.35895376, 0.4352232 , 0.37581183, 0.28736057, 0.28857992,
0.44465289, 0.4401131 , 0.43479979, 0.44325276, 0.50315875,
0.68705236, 0.5876861 , 0.49102124, 0.5602902 , 0.62317442,
0.57240473, 0.78583532, 0.77541517, 0.69910293, 0.68679074,
0.65536561, 0.5637017 , 0.56611116, 0.56543684, 0.53663518,
0.51793381, 0.98101868, 0.66025066, 0.77770331, 0.93235825,
0.7451838 , 0.7356149 , 1.27916041, 1.26780663, 1.08777453,
1.03264314, 1.29540341, 1.3373934 , 1.66962732, 1.5179862 ,
1.52526967, 1.10847494, 1.10960146, 1.13069009, 0.80489176,
0.84084965, 0.81707489, 0.9701507 , 1.20508107, 1.43344929,
1.42137286, 1.02256263, 1.50118428, 1.07926054, 1.23248269,
1.2518788 , 0.83447847, 1.22847598, 1.26770364, 1.55469485,
0.88444105, 0.83784687, 0.78634117, 0.93678337, 0.8632103 ,
2.60502496, 0.80482131, 0.9292601 , 0.94654267, 1.40804765,
1.12839708, 1.3058936 , 1.25100988, 1.1963217 , 0.92075031,
0.82556762, 0.76009077, 1.05373835, 0.75912251, 0.81624164,
0.75380212, 0.76683994, 0.84925331, 1.39901102, 0.99519 ,
1.0258623 , 0.80459167, 0.78357945, 1.05931508, 1.44306612,
2.06199125, 1.35499058, 0.91149231, 0.97072058, 1.04437117,
2.0395103 , 1.35865108, 1.74415052, 1.30162085, 2.07601469,
1.48632419, 1.41457674, 1.41988454, 2.53311422, 1.33750898,
0.81665428, 0.83025989, 0.85986794, 1.40197739, 1.37673419,
1.47734834, 1.32502292, 1.67955691, 1.17965851, 1.29714208,
1.15972812, 1.19675745, 1.1023861 , 1.14748887, 1.18783746,
1.20259173, 1.38173853, 1.50869383, 1.48526367, 0.9069719 ,
0.959725 , 0.77175699, 0.79167109, 0.77491254, 1.02293349,
1.36751554, 0.81550134, 0.76778059, 0.75129115, 0.80562778,
1.40042555, 0.74074691, 0.7325015 , 0.76085346, 1.38971132,
1.26524292, 1.37840041, 1.38254438, 0.79805262, 0.72403297,
0.83321361, 0.72074244, 0.70338518, 0.72840253, 0.68674675,
0.80224401, 1.30733991, 0.77440879, 0.75662519, 0.78734577,
0.6631577 , 0.68938528, 0.65776579, 0.71331126, 0.83442733,
0.83801423, 0.69332151, 0.97755969, 0.7244367 , 0.75401479,
0.69179507, 0.71702348, 0.74940516, 0.80993498, 0.77221189,
0.70334382, 0.64361075, 0.73948301, 1.02182981, 1.09936081,
1.24818217, 0.96553881, 0.92672746, 0.37383221, 0.39676601,
0.54401266, 0.57089378, 0.3474446 , 0.32475682, 0.42793685,
0.36054167, 0.56852605, 0.57055833, 0.72316853, 0.66992334,
0.58552026])
对数据的某一列或某几列进行标准化
preprocessing.scale(boston.target)
array([ 0.15968566, -0.10152429, 1.32424667, 1.18275795, 1.48750288,
0.6712218 , 0.03996443, 0.49708184, -0.65659542, -0.39538548,
-0.81985164, -0.39538548, -0.09064054, -0.23212926, -0.47157171,
-0.286548 , 0.06173193, -0.54775795, -0.25389676, -0.47157171,
-0.97222411, -0.31919924, -0.79808414, -0.87427038, -0.75454915,
-0.93957286, -0.64571167, -0.84161913, -0.44980422, -0.16682677,
-1.07017784, -0.87427038, -1.0157591 , -1.02664285, -0.98310786,
-0.39538548, -0.27566425, -0.16682677, 0.23587189, 0.89978051,
1.34601416, 0.4426631 , 0.30117438, 0.23587189, -0.14505928,
-0.35185049, -0.27566425, -0.64571167, -0.88515413, -0.34096674,
-0.30831549, -0.22124551, 0.26852314, 0.09438317, -0.39538548,
1.4004329 , 0.23587189, 0.98685049, 0.08349942, -0.31919924,
-0.41715297, -0.71101416, -0.0362218 , 0.26852314, 1.13922296,
0.10526692, -0.34096674, -0.0579893 , -0.55864169, -0.17771052,
0.18145315, -0.09064054, 0.02908069, 0.09438317, 0.17056941,
-0.12329178, -0.27566425, -0.18859427, -0.14505928, -0.24301301,
0.59503557, 0.14880191, 0.24675564, 0.03996443, 0.14880191,
0.4426631 , -0.00357056, -0.0362218 , 0.11615067, 0.6712218 ,
0.00731319, -0.0579893 , 0.03996443, 0.26852314, -0.21036176,
0.63857056, -0.12329178, 1.75959658, 2.31466771, 1.16099045,
0.54061683, 0.43177935, -0.42803672, -0.35185049, -0.2647805 ,
-0.33008299, -0.33008299, -0.23212926, -0.29743175, -0.34096674,
-0.09064054, 0.02908069, -0.40626922, -0.41715297, -0.43892047,
-0.46068796, -0.14505928, -0.36273423, -0.23212926, -0.35185049,
-0.0579893 , -0.24301301, -0.22124551, -0.56952544, -0.40626922,
-0.12329178, -0.7436654 , -0.68924667, -0.49333921, -0.89603787,
-0.36273423, -0.31919924, 0.05084818, -0.44980422, -0.75454915,
-0.48245546, -0.55864169, -0.59129294, -1.00487535, -0.5151067 ,
-0.92868912, -0.88515413, -0.9939916 , -0.75454915, -1.16813157,
-0.95045661, -0.75454915, -0.86338663, -0.5151067 , -0.77631665,
-0.11240804, -0.31919924, -0.7872004 , -0.34096674, -0.60217668,
-0.75454915, -1.02664285, 2.04257402, 0.1923369 , 0.08349942,
0.48619809, 2.98946007, 2.98946007, 2.98946007, 0.01819694,
0.26852314, 2.98946007, 0.13791816, 0.13791816, -0.02533805,
-0.55864169, -0.37361798, 0.06173193, 0.11615067, 0.00731319,
0.74740804, 0.07261568, 0.22498815, 0.80182678, 1.59634036,
1.8793178 , 1.48750288, 1.6725266 , 1.08480422, 0.4208956 ,
0.76917553, 2.98946007, 1.03038548, 0.79094303, 1.34601416,
1.57457287, 0.86712926, 1.50927038, 0.93243175, 0.71475679,
2.98946007, 1.1718742 , 0.84536177, 1.31336292, 1.34601416,
1.12833921, 0.17056941, 2.1514115 , 2.82620386, 2.98946007,
0.00731319, 0.20322065, -0.00357056, 0.20322065, -0.27566425,
-0.09064054, -0.35185049, -0.01445431, 0.60591932, 0.12703442,
0.26852314, 0.08349942, 0.6712218 , -0.11240804, 0.05084818,
0.45354685, -0.09064054, 0.54061683, 0.82359427, 2.42350519,
2.98946007, 1.63987535, 0.98685049, 2.6302964 , 0.97596674,
0.1923369 , 0.99773424, 2.08610901, 2.80443636, 0.70387305,
0.15968566, 0.27940688, 0.97596674, 0.12703442, 0.08349942,
-0.0579893 , -0.2647805 , -0.0362218 , 0.12703442, -0.5368742 ,
-0.43892047, 0.1923369 , -0.22124551, 0.2141044 , 0.39912811,
0.20322065, 0.24675564, 0.76917553, 2.20583024, -0.06887304,
-0.17771052, 2.33643521, 2.98946007, 1.46573539, 0.82359427,
1.22629294, 2.23848148, 2.8588551 , 0.921548 , 1.52015413,
0.02908069, 0.88889676, 2.98946007, 2.28201647, -0.19947802,
-0.15594303, 0.29029063, 0.20322065, 1.37866541, 1.07392047,
1.03038548, 1.16099045, 1.1501067 , 0.71475679, 1.36778166,
2.48880768, 1.4004329 , 2.55411016, 2.98946007, 1.05215297,
-0.0579893 , -0.2647805 , 0.07261568, -0.02533805, 0.24675564,
0.64945431, 1.60722411, 0.58415182, 0.14880191, -0.09064054,
0.66033806, 0.49708184, -0.24301301, -0.00357056, 0.70387305,
0.24675564, -0.0579893 , 0.4208956 , 1.1501067 , 1.47661914,
0.63857056, 1.18275795, 0.61680306, 0.02908069, -0.24301301,
-0.70013041, -0.04710555, -0.34096674, -0.10152429, 0.13791816,
-0.68924667, -0.5151067 , -0.29743175, 0.06173193, -0.16682677,
0.13791816, 0.06173193, -0.23212926, -0.43892047, 0.26852314,
0.22498815, 0.05084818, -0.0362218 , -0.35185049, 0.00731319,
-0.29743175, -0.59129294, -0.34096674, -0.0362218 , -0.19947802,
-0.15594303, -0.33008299, -0.43892047, -0.21036176, -0.38450173,
-0.41715297, 1.10657171, -0.65659542, 0.14880191, 0.9433155 ,
-0.54775795, -0.58040919, 0.06173193, 0.2141044 , 0.4426631 ,
0.03996443, 0.17056941, -0.42803672, 0.82359427, -0.47157171,
-0.21036176, -0.5151067 , -0.09064054, 0.01819694, 0.00731319,
0.26852314, -0.286548 , -0.18859427, -0.62394418, -0.06887304,
0.54061683, -0.06887304, 0.06173193, 2.98946007, 2.98946007,
2.98946007, 2.98946007, 2.98946007, -0.95045661, -0.95045661,
-0.81985164, -0.93957286, -1.00487535, -1.02664285, -1.34227153,
-1.32050404, -1.2660853 , -1.22255031, -1.11371283, -1.494644 ,
-1.66878396, -1.30962029, -1.64701647, -1.34227153, -1.20078281,
-0.80896789, 0.07261568, -1.39669027, -0.95045661, -1.07017784,
-1.02664285, -1.09194533, -1.52729524, -1.90822641, -1.76673769,
-1.84292393, -1.66878396, -1.13548032, -1.54906274, -1.52729524,
-1.90822641, -1.15724782, 0.58415182, -0.58040919, 0.54061683,
-0.81985164, -0.58040919, -0.50422295, -0.67836292, -1.69055146,
-1.66878396, -1.63613272, -1.32050404, -1.494644 , -1.53817899,
-0.63482793, -0.90692162, -0.18859427, -0.9939916 , -1.17901531,
-1.54906274, -1.34227153, -1.2660853 , -1.25520155, -1.41845777,
-0.87427038, -0.91780537, -0.70013041, -0.89603787, -1.17901531,
-0.9939916 , -1.40757402, -1.50552775, -1.53817899, -1.05929409,
-1.30962029, -0.59129294, -0.44980422, -0.77631665, -1.27696904,
-1.16813157, -0.83073539, -1.08106158, -0.91780537, -1.03752659,
-0.9939916 , -0.79808414, -0.70013041, -0.5151067 , -0.83073539,
-0.91780537, -1.07017784, -0.98310786, -0.83073539, -0.27566425,
-0.66747917, -0.52599045, -0.33008299, -0.25389676, -0.12329178,
-0.286548 , -0.38450173, -0.37361798, -0.37361798, -0.2647805 ,
-0.286548 , -0.31919924, 0.07261568, 0.79094303, -0.95045661,
-1.00487535, -0.63482793, -1.14636407, -0.86338663, -0.12329178,
0.05084818, 0.12703442, 0.26852314, -0.07975679, -0.21036176,
-0.14505928, -0.37361798, -0.21036176, -0.79808414, -1.69055146,
-1.57083023, -0.97222411, -0.2647805 , -0.07975679, 0.2141044 ,
0.06173193, -0.30831549, -0.46068796, -0.14505928, -0.54775795,
-0.62394418, -0.01445431, -0.21036176, 0.14880191, -0.0579893 ,
-1.15724782])
std.get_params()**<font color = orange>#查看当前参数值
{'copy': True, 'with_mean': False, 'with_std': True}
std.set_params(copy=False)**<font color = orange>#设置参数值
StandardScaler(copy=False, with_mean=False)
std.get_params()
{'copy': False, 'with_mean': False, 'with_std': True}
std.fit(boston.data)
StandardScaler(copy=False, with_mean=False)
std.scale_**<font color = orange>#标准化后的尺度
array([8.59304135e+00, 2.32993957e+01, 6.85357058e+00, 2.53742935e-01,
1.15763115e-01, 7.01922514e-01, 2.81210326e+01, 2.10362836e+00,
8.69865112e+00, 1.68370495e+02, 2.16280519e+00, 9.12046075e+01,
7.13400164e+00])
std.mean_**<font color = orange>#标准化的期望
array([3.61352356e+00, 1.13636364e+01, 1.11367787e+01, 6.91699605e-02,
5.54695059e-01, 6.28463439e+00, 6.85749012e+01, 3.79504269e+00,
9.54940711e+00, 4.08237154e+02, 1.84555336e+01, 3.56674032e+02,
1.26530632e+01])
StandardScaler类的算法
**inverse_transform(X,copy]) 将数据进行逆变换
partial_fit(X [, y]) 在线计算数据特征用于后续拟合
fit(X[, y]) 计算数据特征用于后续拟合
transform(x[, y.copy]) 用于模型设定进行转换
fit_transform(X[, y]) 计算数据特征，并且进行转换
get_params([deep]) 获取模型的参数设定
set_params(** params) 设置模型参数**
scaler = preprocessing.MaxAbsScaler((1,10))
scaler.fit_transform(boston_df)
D:\Anaconda3\lib\site-packages\sklearn\utils\validation.py:70: FutureWarning: Pass copy=(1, 10) as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error
warnings.warn(f"Pass {args_msg} as keyword args. From version "
array([[7.10302306e-05, 1.80000000e-01, 8.32732516e-02, ...,
6.95454545e-01, 1.00000000e+00, 1.31156176e-01],
[3.06936012e-04, 0.00000000e+00, 2.54866619e-01, ...,
8.09090909e-01, 1.00000000e+00, 2.40716355e-01],
[3.06711233e-04, 0.00000000e+00, 2.54866619e-01, ...,
8.09090909e-01, 9.89745528e-01, 1.06136423e-01],
...,
[6.82879242e-04, 0.00000000e+00, 4.30064888e-01, ...,
9.54545455e-01, 1.00000000e+00, 1.48538320e-01],
[1.23167768e-03, 0.00000000e+00, 4.30064888e-01, ...,
9.54545455e-01, 9.91307634e-01, 1.70661048e-01],
[5.32839119e-04, 0.00000000e+00, 4.30064888e-01, ...,
9.54545455e-01, 1.00000000e+00, 2.07532262e-01]])
正则表达（Normalization)/归一化/范数化石机器学习领域提出的基于向量空间模型上的一个转换，
经常被使用在分类与聚类中。
sklearn.preprocessing.normalize(
X, axis = 1, copy = True
norm = ‘12’# : ‘11’,‘12’,or ‘max’,用来正则化的具体范数
return_norm = False #: 是否返回所使用的范数)
x = [[-1,-1,2]]
x_normalized = preprocessing.normalize(x,norm=‘12’,return_norm = True)
x_normalized