线性回归

波士顿房价预测案例

步骤

  • 导入数据
  • 数据分割
  • 数据标准化
  • 正规方程预测
  • 梯度下降预测
# 导入模块

import pandas as pd # 导入数据

from sklearn.model_selection import train_test_split # 数据分割

from sklearn.preprocessing import StandardScaler # 数据标准化

from sklearn.linear_model import LinearRegression, SGDRegressor, Ridge # 正规方程，梯度下降, 岭回归

from sklearn.metrics import mean_squared_error # 均方差

import numpy as np


# 读取Boston房价数据

boston = pd.read_csv("./boston_house_prices.csv")

y = boston["MEDV"] # MEDV为离散型目标值

x = boston.drop(["MEDV"],axis=1) # 其他数据为特征值


x
.dataframe tbody tr th:only-of-type { vertical-align: middle }
\3cpre>\3ccode>.dataframe tbody tr th { vertical-align: top }
.dataframe thead th { text-align: right }

CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO B LSTAT
0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 396.90 4.98
1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 396.90 9.14
2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 392.83 4.03
3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 394.63 2.94
4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 396.90 5.33
... ... ... ... ... ... ... ... ... ... ... ... ... ...
501 0.06263 0.0 11.93 0 0.573 6.593 69.1 2.4786 1 273 21.0 391.99 9.67
502 0.04527 0.0 11.93 0 0.573 6.120 76.7 2.2875 1 273 21.0 396.90 9.08
503 0.06076 0.0 11.93 0 0.573 6.976 91.0 2.1675 1 273 21.0 396.90 5.64
504 0.10959 0.0 11.93 0 0.573 6.794 89.3 2.3889 1 273 21.0 393.45 6.48
505 0.04741 0.0 11.93 0 0.573 6.030 80.8 2.5050 1 273 21.0 396.90 7.88

506 rows × 13 columns

# 数据标准化需要传入二维数组，所以需要改变目标值的形状

y = np.array(y).reshape(-1, 1)


# 划分测试集和训练集

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)


# 特征值标准化

std_x = StandardScaler().fit(x_train)

x_train = std_x.transform(x_train)

x_test = std_x.transform(x_test)


# 因为特征值标准化后，传入模型的系数会增大，所以目标值也需要进行标准化

std_y = StandardScaler().fit(y_train)

y_train = std_y.transform(y_train)

y_test = std_y.transform(y_test)


# 实例化线性回归

lr = LinearRegression()

# 传入测试集训练模型

lr.fit(x_train,y_train)

#sk-container-id-1 { color: rgba(0, 0, 0, 1); background-color: rgba(255, 255, 255, 1) }
#sk-container-id-1 pre { padding: 0 }
#sk-container-id-1 div.sk-toggleable { background-color: rgba(255, 255, 255, 1) }
#sk-container-id-1 label.sk-toggleable__label { cursor: pointer; display: block; width: 100%; margin-bottom: 0; padding: 0.3em; box-sizing: border-box; text-align: center }
#sk-container-id-1 label.sk-toggleable__label-arrow:before { content: "▸"; float: left; margin-right: 0.25em; color: rgba(105, 105, 105, 1) }
#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before { color: rgba(0, 0, 0, 1) }
#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before { color: rgba(0, 0, 0, 1) }
#sk-container-id-1 div.sk-toggleable__content { max-height: 0; max-width: 0; overflow: hidden; text-align: left; background-color: rgba(240, 248, 255, 1) }
#sk-container-id-1 div.sk-toggleable__content pre { margin: 0.2em; color: rgba(0, 0, 0, 1); border-radius: 0.25em; background-color: rgba(240, 248, 255, 1) }
#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content { max-height: 200px; max-width: 100%; overflow: auto }
#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before { content: "▾" }
#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-1 input.sk-hidden--visually { border: 0; clip: rect(1px, 1px, 1px, 1px); height: 1px; margin: -1px; overflow: hidden; padding: 0; position: absolute; width: 1px }
#sk-container-id-1 div.sk-estimator { font-family: monospace; background-color: rgba(240, 248, 255, 1); border: 1px dotted rgba(0, 0, 0, 1); border-radius: 0.25em; box-sizing: border-box; margin-bottom: 0.5em }
#sk-container-id-1 div.sk-estimator:hover { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-1 div.sk-parallel-item::after { content: ""; width: 100%; border-bottom: 1px solid rgba(128, 128, 128, 1); flex-grow: 1 }
#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-1 div.sk-serial::before { content: ""; position: absolute; border-left: 1px solid rgba(128, 128, 128, 1); box-sizing: border-box; top: 0; bottom: 0; left: 50%; z-index: 0 }
#sk-container-id-1 div.sk-serial { display: flex; flex-direction: column; align-items: center; background-color: rgba(255, 255, 255, 1); padding-right: 0.2em; padding-left: 0.2em; position: relative }
#sk-container-id-1 div.sk-item { position: relative; z-index: 1 }
#sk-container-id-1 div.sk-parallel { display: flex; align-items: stretch; justify-content: center; background-color: rgba(255, 255, 255, 1); position: relative }
#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before { content: ""; position: absolute; border-left: 1px solid rgba(128, 128, 128, 1); box-sizing: border-box; top: 0; bottom: 0; left: 50%; z-index: -1 }
#sk-container-id-1 div.sk-parallel-item { display: flex; flex-direction: column; z-index: 1; position: relative; background-color: rgba(255, 255, 255, 1) }
#sk-container-id-1 div.sk-parallel-item:first-child::after { align-self: flex-end; width: 50% }
#sk-container-id-1 div.sk-parallel-item:last-child::after { align-self: flex-start; width: 50% }
#sk-container-id-1 div.sk-parallel-item:only-child::after { width: 0 }
#sk-container-id-1 div.sk-dashed-wrapped { border: 1px dashed rgba(128, 128, 128, 1); margin: 0 0.4em 0.5em; box-sizing: border-box; padding-bottom: 0.4em; background-color: rgba(255, 255, 255, 1) }
#sk-container-id-1 div.sk-label label { font-family: monospace; font-weight: bold; display: inline-block; line-height: 1.2em }
#sk-container-id-1 div.sk-label-container { text-align: center }
#sk-container-id-1 div.sk-container { display: inline-block !important; position: relative }
#sk-container-id-1 div.sk-text-repr-fallback { display: none } 

LinearRegression()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

LinearRegression 
LinearRegression()
# 查看线性回归的回归系数

lr.coef_


array([[-0.11432612,  0.12922939,  0.05168773,  0.0306429 , -0.27800333,

         0.26465189,  0.02894241, -0.34962992,  0.31569604, -0.24717234,

        -0.26784233,  0.11032066, -0.41354896]])


# 线性回归预测测试集的目标值，std_y.inverse_transform：返回标准化之前的值（反标准化）

y_lr_predict = std_y.inverse_transform(lr.predict(x_test))

y_lr_predict


array([[16.88302519],

       [25.67464426],

       [24.11685261],

       [23.56287231],

       [33.21442377],

       [17.44428398],

       [25.08538719],

       [14.36188824],

       [23.8507796 ],

       [33.90875038],

       [30.19255243],

       [13.30811675],

       [28.60383216],

       [34.6094617 ],

       [27.32666762],

       [24.88310221],

       [21.97377504],

       [14.36080511],

       [15.19834144],

       [18.91688837],

       [14.39284881],

       [37.4279415 ],

       [28.85628069],

       [23.47343089],

       [30.65979144],

       [20.77177982],

       [21.29899429],

       [13.81410752],

       [24.36591359],

       [26.91067836],

       [19.39456288],

       [32.1620506 ],

       [19.55908532],

       [24.32677646],

       [31.64841534],

       [30.24445789],

       [32.6601561 ],

       [25.45770231],

       [24.36812628],

       [24.89892187],

       [39.51204317],

       [18.25845589],

       [30.78050699],

       [32.2023306 ],

       [43.40712056],

       [25.5830554 ],

       [24.18175285],

       [22.22948918],

       [16.30284868],

       [27.20443307],

       [ 4.3558633 ],

       [18.24971547],

       [17.84402513],

       [14.26170574],

       [13.64455453],

       [34.67825232],

       [ 8.26805278],

       [23.65092602],

       [ 6.3965518 ],

       [21.25451713],

       [15.71560149],

       [29.29210802],

       [29.4266973 ],

       [19.91658528],

       [14.95841515],

       [20.88449625],

       [28.59263417],

       [23.78937845],

       [23.4489951 ],

       [11.0440392 ],

       [19.4491492 ],

       [15.48416226],

       [18.68260651],

       [24.20199734],

       [15.78191346],

       [14.11243619],

       [22.94901405],

       [24.02549373],

       [21.11185284],

       [28.57665473],

       [ 7.45548609],

       [22.77052456],

       [ 3.44149312],

       [15.93067248],

       [25.72200382],

       [22.56825235],

       [32.70873719],

       [17.86289514],

       [24.49691931],

       [35.25395986],

       [26.98360999],

       [17.51000169],

       [28.08531514],

       [21.15268973],

       [24.73138251],

       [-4.82364972],

       [21.34031184],

       [21.89560028],

       [16.35765837],

       [35.32764197],

       [40.95997005],

       [23.59853443],

       [19.92593809],

       [34.43871021],

       [21.37340243],

       [20.48191389],

       [23.77537201],

       [28.67150943],

       [40.73850694],

       [29.38542779],

       [21.25032737],

       [22.15530128],

       [31.1447006 ],

       [17.18008197],

       [38.09276107],

       [18.17714902],

       [26.01850231],

       [13.73181577],

       [12.47399654],

       [27.01659936],

       [18.62962667],

       [11.26915964],

       [19.48824649],

       [23.64510406],

       [18.88328087],

       [19.49037977],

       [13.58238162]])


# 线性回归预测的均方差（损失值）

loss_lr = mean_squared_error(std_y.inverse_transform(y_test), y_lr_predict)

loss_lr


27.89401984711536


# 实例化梯度下降回归

sgd = SGDRegressor()

sgd.fit(x_train, y_train)


D:\DeveloperTools\Anaconda\lib\site-packages\sklearn\utils\validation.py:1143: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().

  y = column_or_1d(y, warn=True)

#sk-container-id-2 { color: rgba(0, 0, 0, 1); background-color: rgba(255, 255, 255, 1) }
#sk-container-id-2 pre { padding: 0 }
#sk-container-id-2 div.sk-toggleable { background-color: rgba(255, 255, 255, 1) }
#sk-container-id-2 label.sk-toggleable__label { cursor: pointer; display: block; width: 100%; margin-bottom: 0; padding: 0.3em; box-sizing: border-box; text-align: center }
#sk-container-id-2 label.sk-toggleable__label-arrow:before { content: "▸"; float: left; margin-right: 0.25em; color: rgba(105, 105, 105, 1) }
#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before { color: rgba(0, 0, 0, 1) }
#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before { color: rgba(0, 0, 0, 1) }
#sk-container-id-2 div.sk-toggleable__content { max-height: 0; max-width: 0; overflow: hidden; text-align: left; background-color: rgba(240, 248, 255, 1) }
#sk-container-id-2 div.sk-toggleable__content pre { margin: 0.2em; color: rgba(0, 0, 0, 1); border-radius: 0.25em; background-color: rgba(240, 248, 255, 1) }
#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content { max-height: 200px; max-width: 100%; overflow: auto }
#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before { content: "▾" }
#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-2 input.sk-hidden--visually { border: 0; clip: rect(1px, 1px, 1px, 1px); height: 1px; margin: -1px; overflow: hidden; padding: 0; position: absolute; width: 1px }
#sk-container-id-2 div.sk-estimator { font-family: monospace; background-color: rgba(240, 248, 255, 1); border: 1px dotted rgba(0, 0, 0, 1); border-radius: 0.25em; box-sizing: border-box; margin-bottom: 0.5em }
#sk-container-id-2 div.sk-estimator:hover { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-2 div.sk-parallel-item::after { content: ""; width: 100%; border-bottom: 1px solid rgba(128, 128, 128, 1); flex-grow: 1 }
#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-2 div.sk-serial::before { content: ""; position: absolute; border-left: 1px solid rgba(128, 128, 128, 1); box-sizing: border-box; top: 0; bottom: 0; left: 50%; z-index: 0 }
#sk-container-id-2 div.sk-serial { display: flex; flex-direction: column; align-items: center; background-color: rgba(255, 255, 255, 1); padding-right: 0.2em; padding-left: 0.2em; position: relative }
#sk-container-id-2 div.sk-item { position: relative; z-index: 1 }
#sk-container-id-2 div.sk-parallel { display: flex; align-items: stretch; justify-content: center; background-color: rgba(255, 255, 255, 1); position: relative }
#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before { content: ""; position: absolute; border-left: 1px solid rgba(128, 128, 128, 1); box-sizing: border-box; top: 0; bottom: 0; left: 50%; z-index: -1 }
#sk-container-id-2 div.sk-parallel-item { display: flex; flex-direction: column; z-index: 1; position: relative; background-color: rgba(255, 255, 255, 1) }
#sk-container-id-2 div.sk-parallel-item:first-child::after { align-self: flex-end; width: 50% }
#sk-container-id-2 div.sk-parallel-item:last-child::after { align-self: flex-start; width: 50% }
#sk-container-id-2 div.sk-parallel-item:only-child::after { width: 0 }
#sk-container-id-2 div.sk-dashed-wrapped { border: 1px dashed rgba(128, 128, 128, 1); margin: 0 0.4em 0.5em; box-sizing: border-box; padding-bottom: 0.4em; background-color: rgba(255, 255, 255, 1) }
#sk-container-id-2 div.sk-label label { font-family: monospace; font-weight: bold; display: inline-block; line-height: 1.2em }
#sk-container-id-2 div.sk-label-container { text-align: center }
#sk-container-id-2 div.sk-container { display: inline-block !important; position: relative }
#sk-container-id-2 div.sk-text-repr-fallback { display: none } 

SGDRegressor()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

SGDRegressor 
SGDRegressor()
# 查看梯度下降回归的回归系数

sgd.coef_


array([-0.09761234,  0.08895746, -0.02421963,  0.02879482, -0.17976106,

        0.30861884, -0.00250273, -0.27224473,  0.12435245, -0.0780263 ,

       -0.24480836,  0.12012805, -0.38888841])


# 梯度下降回归预测测试集的目标值，std_y.inverse_transform：返回标准化之前的值（反标准化）

y_sgd_predict = std_y.inverse_transform(sgd.predict(x_test).reshape(-1,1))

y_sgd_predict


array([[15.21420286],

       [24.63693863],

       [24.39828101],

       [24.13982716],

       [32.78620978],

       [17.93179618],

       [26.15279053],

       [14.48966421],

       [23.47566531],

       [33.17239509],

       [31.84452891],

       [12.45562282],

       [27.95300787],

       [33.80241039],

       [28.49956651],

       [24.66480492],

       [22.36941513],

       [12.77314567],

       [16.19679874],

       [19.55497851],

       [16.56475828],

       [37.33119072],

       [28.7775393 ],

       [20.96986273],

       [30.61621249],

       [21.02209026],

       [21.7295418 ],

       [12.81210827],

       [24.5110437 ],

       [26.43938704],

       [18.35264658],

       [32.65009183],

       [18.43526582],

       [23.00618081],

       [31.7400822 ],

       [29.04743561],

       [33.05208407],

       [25.74448792],

       [24.50083552],

       [25.60223044],

       [39.54513459],

       [17.1185942 ],

       [31.03740088],

       [31.08938082],

       [43.05539907],

       [25.73953331],

       [24.94663261],

       [22.54125585],

       [18.28413619],

       [26.10355346],

       [ 6.00742562],

       [17.91294014],

       [18.30811745],

       [12.44053594],

       [12.80928627],

       [35.3744289 ],

       [ 9.09787342],

       [22.93659674],

       [ 5.43064498],

       [21.74836536],

       [14.35146387],

       [29.01003788],

       [29.08635743],

       [22.73088123],

       [14.63525207],

       [21.85792442],

       [27.65781677],

       [23.792957  ],

       [24.6814747 ],

       [10.92976509],

       [19.83990001],

       [15.96966791],

       [18.14900105],

       [25.20832651],

       [13.27422495],

       [14.30232772],

       [23.11242467],

       [25.77201334],

       [19.68444307],

       [28.57611678],

       [ 7.63364889],

       [20.4696819 ],

       [ 2.27690801],

       [16.55235057],

       [25.58622675],

       [22.77961526],

       [32.47346299],

       [17.77241159],

       [22.97811939],

       [36.08937688],

       [26.73491284],

       [18.29474336],

       [29.46454709],

       [21.71750293],

       [26.04970043],

       [-5.49919448],

       [22.22155065],

       [22.98441588],

       [15.12536374],

       [35.73982924],

       [40.87874356],

       [23.690842  ],

       [20.5993433 ],

       [35.69123855],

       [20.68804356],

       [20.94190843],

       [26.02227126],

       [31.17410177],

       [40.95630421],

       [29.90544672],

       [23.50763821],

       [22.27432439],

       [29.64014839],

       [16.78407484],

       [38.12893576],

       [17.69781499],

       [25.22891716],

       [14.21875615],

       [12.55974345],

       [26.99891265],

       [17.65595579],

       [ 8.4159419 ],

       [19.90142312],

       [22.80759632],

       [19.16843753],

       [19.42995139],

       [14.04081021]])


# 梯度下降回归预测的均方差（损失值）

loss_sgd = mean_squared_error(std_y.inverse_transform(y_test), y_sgd_predict)

loss_sgd


28.05592202385498


# 实例化岭回归 param:alpha(正则化力度)

rd = Ridge(alpha=1.0)


# 传入训练集 训练模型

rd.fit(x_train,y_train)

#sk-container-id-3 { color: rgba(0, 0, 0, 1); background-color: rgba(255, 255, 255, 1) }
#sk-container-id-3 pre { padding: 0 }
#sk-container-id-3 div.sk-toggleable { background-color: rgba(255, 255, 255, 1) }
#sk-container-id-3 label.sk-toggleable__label { cursor: pointer; display: block; width: 100%; margin-bottom: 0; padding: 0.3em; box-sizing: border-box; text-align: center }
#sk-container-id-3 label.sk-toggleable__label-arrow:before { content: "▸"; float: left; margin-right: 0.25em; color: rgba(105, 105, 105, 1) }
#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before { color: rgba(0, 0, 0, 1) }
#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before { color: rgba(0, 0, 0, 1) }
#sk-container-id-3 div.sk-toggleable__content { max-height: 0; max-width: 0; overflow: hidden; text-align: left; background-color: rgba(240, 248, 255, 1) }
#sk-container-id-3 div.sk-toggleable__content pre { margin: 0.2em; color: rgba(0, 0, 0, 1); border-radius: 0.25em; background-color: rgba(240, 248, 255, 1) }
#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content { max-height: 200px; max-width: 100%; overflow: auto }
#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before { content: "▾" }
#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-3 input.sk-hidden--visually { border: 0; clip: rect(1px, 1px, 1px, 1px); height: 1px; margin: -1px; overflow: hidden; padding: 0; position: absolute; width: 1px }
#sk-container-id-3 div.sk-estimator { font-family: monospace; background-color: rgba(240, 248, 255, 1); border: 1px dotted rgba(0, 0, 0, 1); border-radius: 0.25em; box-sizing: border-box; margin-bottom: 0.5em }
#sk-container-id-3 div.sk-estimator:hover { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-3 div.sk-parallel-item::after { content: ""; width: 100%; border-bottom: 1px solid rgba(128, 128, 128, 1); flex-grow: 1 }
#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label { background-color: rgba(212, 235, 255, 1) }
#sk-container-id-3 div.sk-serial::before { content: ""; position: absolute; border-left: 1px solid rgba(128, 128, 128, 1); box-sizing: border-box; top: 0; bottom: 0; left: 50%; z-index: 0 }
#sk-container-id-3 div.sk-serial { display: flex; flex-direction: column; align-items: center; background-color: rgba(255, 255, 255, 1); padding-right: 0.2em; padding-left: 0.2em; position: relative }
#sk-container-id-3 div.sk-item { position: relative; z-index: 1 }
#sk-container-id-3 div.sk-parallel { display: flex; align-items: stretch; justify-content: center; background-color: rgba(255, 255, 255, 1); position: relative }
#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before { content: ""; position: absolute; border-left: 1px solid rgba(128, 128, 128, 1); box-sizing: border-box; top: 0; bottom: 0; left: 50%; z-index: -1 }
#sk-container-id-3 div.sk-parallel-item { display: flex; flex-direction: column; z-index: 1; position: relative; background-color: rgba(255, 255, 255, 1) }
#sk-container-id-3 div.sk-parallel-item:first-child::after { align-self: flex-end; width: 50% }
#sk-container-id-3 div.sk-parallel-item:last-child::after { align-self: flex-start; width: 50% }
#sk-container-id-3 div.sk-parallel-item:only-child::after { width: 0 }
#sk-container-id-3 div.sk-dashed-wrapped { border: 1px dashed rgba(128, 128, 128, 1); margin: 0 0.4em 0.5em; box-sizing: border-box; padding-bottom: 0.4em; background-color: rgba(255, 255, 255, 1) }
#sk-container-id-3 div.sk-label label { font-family: monospace; font-weight: bold; display: inline-block; line-height: 1.2em }
#sk-container-id-3 div.sk-label-container { text-align: center }
#sk-container-id-3 div.sk-container { display: inline-block !important; position: relative }
#sk-container-id-3 div.sk-text-repr-fallback { display: none } 

Ridge()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Ridge 
Ridge()
# 查看岭回归的回归系数

rd.coef_


array([[-0.11307323,  0.12670886,  0.0472335 ,  0.03097279, -0.27277927,

         0.26649452,  0.02738887, -0.34543899,  0.30352311, -0.23553989,

        -0.26624461,  0.11041044, -0.4112231 ]])


# 岭回归预测测试集的目标值，std_y.inverse_transform：返回标准化之前的值（反标准化）

y_rd_predict = std_y.inverse_transform(rd.predict(x_test))

y_rd_predict


array([[16.81586993],

       [25.62225283],

       [24.13239652],

       [23.60178301],

       [33.17482664],

       [17.47603707],

       [25.12448624],

       [14.3927178 ],

       [23.82242142],

       [33.83569284],

       [30.25910195],

       [13.28992719],

       [28.54601232],

       [34.54914571],

       [27.36491618],

       [24.87707782],

       [22.00096365],

       [14.31750595],

       [15.26655896],

       [18.95164011],

       [14.52104908],

       [37.38819398],

       [28.82792081],

       [23.3211182 ],

       [30.6343198 ],

       [20.80233876],

       [21.31839148],

       [13.79005679],

       [24.3590396 ],

       [26.87702832],

       [19.35529157],

       [32.16020072],

       [19.52355909],

       [24.26581358],

       [31.63175652],

       [30.17323569],

       [32.66670796],

       [25.47912641],

       [24.36217689],

       [24.91701584],

       [39.47302165],

       [18.22458912],

       [30.75058024],

       [32.14915944],

       [43.35075081],

       [25.58142763],

       [24.22487493],

       [22.23864659],

       [16.45656221],

       [27.14231857],

       [ 4.52270441],

       [18.23427535],

       [17.87417222],

       [14.1986027 ],

       [13.62643288],

       [34.69768313],

       [ 8.34275415],

       [23.6132958 ],

       [ 6.38923846],

       [21.27558839],

       [15.66185343],

       [29.25676316],

       [29.39607496],

       [20.06328838],

       [14.96702673],

       [20.93444425],

       [28.53639958],

       [23.76724172],

       [23.49637722],

       [11.0745397 ],

       [19.48381901],

       [15.51875938],

       [18.65960692],

       [24.24100427],

       [15.64918598],

       [14.14894164],

       [22.94337728],

       [24.09499988],

       [21.05268108],

       [28.55429725],

       [ 7.51316118],

       [22.62833775],

       [ 3.43124359],

       [15.98036192],

       [25.70480807],

       [22.57033657],

       [32.66624286],

       [17.87124766],

       [24.43818932],

       [35.27111772],

       [26.94613641],

       [17.56269425],

       [28.14078364],

       [21.18918514],

       [24.78403264],

       [-4.78164143],

       [21.36553975],

       [21.94334785],

       [16.31804996],

       [35.31337498],

       [40.90768652],

       [23.60641046],

       [19.94431495],

       [34.4813584 ],

       [21.35327276],

       [20.51324011],

       [23.90175952],

       [28.77241981],

       [40.73752328],

       [29.39270623],

       [21.38182702],

       [22.15806225],

       [31.07297608],

       [17.17452852],

       [38.05954909],

       [18.16913598],

       [25.97549364],

       [13.78567603],

       [12.51045123],

       [26.99932827],

       [18.59193795],

       [11.15468796],

       [19.52228306],

       [23.60713735],

       [18.8861402 ],

       [19.4947593 ],

       [13.61341828]])


# 岭回归预测的均方差（损失值）

loss_rd = mean_squared_error(std_y.inverse_transform(y_test), y_rd_predict)

loss_rd


27.836735080339313

机器学习08DAY的更多相关文章

  1. .NET平台开源项目速览(13)机器学习组件Accord.NET框架功能介绍

    Accord.NET Framework是在AForge.NET项目的基础上封装和进一步开发而来.因为AForge.NET更注重与一些底层和广度,而Accord.NET Framework更注重与机器 ...

  2. 【Machine Learning】机器学习及其基础概念简介

    机器学习及其基础概念简介 作者:白宁超 2016年12月23日21:24:51 摘要:随着机器学习和深度学习的热潮,各种图书层出不穷.然而多数是基础理论知识介绍,缺乏实现的深入理解.本系列文章是作者结 ...

  3. 借助亚马逊S3和RapidMiner将机器学习应用到文本挖掘

    本挖掘典型地运用了机器学习技术,例如聚类,分类,关联规则,和预测建模.这些技术揭示潜在内容中的意义和关系.文本发掘应用于诸如竞争情报,生命科学,客户呼声,媒体和出版,法律和税收,法律实施,情感分析和趋 ...

  4. Android开发学习之路-机器学习库（图像识别）、百度翻译

    对于机器学习也不是了解的很深入,今天无意中在GitHub看到一个star的比较多的库,就用着试一试,效果也还行.比是可能比不上TensorFlow的,但是在Android上用起来比较简单,毕竟Tens ...

  5. 【NLP】基于机器学习角度谈谈CRF（三）

    基于机器学习角度谈谈CRF 作者:白宁超 2016年8月3日08:39:14 [摘要]:条件随机场用于序列标注,数据分割等自然语言处理中,表现出很好的效果.在中文分词.中文人名识别和歧义消解等任务中都 ...

  6. 机器学习实战笔记(Python实现)-08-线性回归

    --------------------------------------------------------------------------------------- 本系列文章为<机器 ...

  7. 机器学习实战笔记(Python实现)-06-AdaBoost

    --------------------------------------------------------------------------------------- 本系列文章为<机器 ...

  8. 机器学习实战笔记(Python实现)-05-支持向量机(SVM)

    --------------------------------------------------------------------------------------- 本系列文章为<机器 ...

  9. 机器学习实战笔记(Python实现)-04-Logistic回归

    --------------------------------------------------------------------------------------- 本系列文章为<机器 ...

  10. 机器学习实战笔记(Python实现)-03-朴素贝叶斯

    --------------------------------------------------------------------------------------- 本系列文章为<机器 ...

随机推荐

  1. Vue.js + TypeScript 项目构建

    一:全局安装vue/cli  npm install -g @vue/cli安装完成后检查版本vue --version 二:构建项目创建文件 vue create projectName 有的刚开始 ...

  2. MYSQL面试题大汇总

    1.数据库的三大范式是什么 1)列不可再分 2)行可以唯一区分 3)表的非主属性不可以依赖与其他表的非主属性,外键约束 2.数据库中的事务 1)什么是事务:多条sql语句,要么都成功,要么都失败 2) ...

  3. 问题记录[ PPOME 修改子屏幕日期弹框，No changes to data, periods undone. Message no. 5A496 ]

    最近在做组织信息增强,将子屏幕嵌入PPOME后,修改日期后会弹出信息框并重置300屏幕的开始日期.且PO13和PP01无异常 刚开始以为7000屏幕配置问题,但是对比后并没发现异常.跟踪消息号发现函数 ...

  4. hdu:最大点权（强连通分量kosaraju）

    Problem Description给定一个有向图,每个点ii有点权a_ia​i​​,请对于每个点ii,找到ii能到达的点中点权的最大值(包括ii点). Input第一行包含一个正整数T(1\leq ...

  5. 访问gitGub速度慢的解决

    转自: https://baijiahao.baidu.com/s?id=1608100091125662190&wfr=spider&for=pc GitHub是一个面向开源及私有软 ...

  6. APP 监听手机键盘是否弹出

    /** * 监听键盘是否弹出 * @param show * @param hide */ export const addEventKeyboardStatus = (show,hide)=> ...

  7. TFlite 多线程并行

    import numpy as np from multiprocessing import Process, Queue def process_data(data, model_TSNet, ts ...

  8. gradle设置

    本地目录: gradle-wrapper.properties distributionUrl=file\:///D:/\.gradle/gradle-7.3-all.zip distribution ...

  9. centos 开启关闭网卡(禁用网卡)

    说明我之前在工作中使用的服务器很多都是多网卡服务器,他可以使用不同的网卡连接不同的网段,但是,由于个别情况突发,有时候可能需要关闭某些网卡,禁止它们访问到网络,也就是需要关闭网卡.步骤1.查看有哪些网 ...

  10. Docker基本概念及命令

    1.Docker三个重要概念:仓库(Repository).镜像(image)和容器(Container) Docker基本用法: docker  命令关键字(COMMAND) 一系列的参数 dock ...