ML之LiR：利用LiR线性回归算法(自定义目标函数MSE和优化器GD)对Boston房价数据集(两特征+归一化)进行回归预测

喜欢ヅ旅行 2022-12-24 05:51 144阅读 0赞

**目录**

利用LiR线性回归算法(自定义目标函数MSE和优化器GD)对Boston房价数据集(两特征+归一化)进行回归预测

设计思路

输出结果

核心代码

--------------------

**相关文章**  
[ML之LiR：利用LiR线性回归算法(自定义目标函数MSE和优化器GD)对Boston房价数据集(两特征+归一化)进行回归预测][ML_LiR_LiR_MSE_GD_Boston]  
[ML之LiR：利用LiR线性回归算法(自定义目标函数MSE和优化器GD)对Boston房价数据集(两特征+归一化)进行回归预测实现][ML_LiR_LiR_MSE_GD_Boston 1]

# 利用LiR线性回归算法(自定义目标函数MSE和优化器GD)对Boston房价数据集(两特征+归一化)进行回归预测 #

## 设计思路 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70][]

## 输出结果 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 2][]

train_boston_data.shape (1460, 81)
       Id  MSSubClass MSZoning  ...  SaleType  SaleCondition SalePrice
    0   1          60       RL  ...        WD         Normal    208500
    1   2          20       RL  ...        WD         Normal    181500
    2   3          60       RL  ...        WD         Normal    223500
    3   4          70       RL  ...        WD        Abnorml    140000
    4   5          60       RL  ...        WD         Normal    250000
    
    [5 rows x 81 columns]
    train_t.head()    LotFrontage  GarageArea  SalePrice
    0         65.0         548     208500
    1         80.0         460     181500
    2         68.0         608     223500
    3         60.0         642     140000
    4         84.0         836     250000
    after scale,train_t.head()    LotFrontage  GarageArea  SalePrice
    0     0.207668    0.386460   0.276159
    1     0.255591    0.324401   0.240397
    2     0.217252    0.428773   0.296026
    3     0.191693    0.452750   0.185430
    4     0.268371    0.589563   0.331126
       LotFrontage  GarageArea
    0     0.207668    0.386460
    1     0.255591    0.324401
    2     0.217252    0.428773
    3     0.191693    0.452750
    4     0.268371    0.589563
                         Id  MSSubClass  LotFrontage  ...    MoSold    YrSold  SalePrice
    Id             1.000000    0.011156    -0.010601  ...  0.021172  0.000712  -0.021917
    MSSubClass     0.011156    1.000000    -0.386347  ... -0.013585 -0.021407  -0.084284
    LotFrontage   -0.010601   -0.386347     1.000000  ...  0.011200  0.007450   0.351799
    LotArea       -0.033226   -0.139781     0.426095  ...  0.001205 -0.014261   0.263843
    OverallQual   -0.028365    0.032628     0.251646  ...  0.070815 -0.027347   0.790982
    OverallCond    0.012609   -0.059316    -0.059213  ... -0.003511  0.043950  -0.077856
    YearBuilt     -0.012713    0.027850     0.123349  ...  0.012398 -0.013618   0.522897
    YearRemodAdd  -0.021998    0.040581     0.088866  ...  0.021490  0.035743   0.507101
    MasVnrArea    -0.050298    0.022936     0.193458  ... -0.005965 -0.008201   0.477493
    BsmtFinSF1    -0.005024   -0.069836     0.233633  ... -0.015727  0.014359   0.386420
    BsmtFinSF2    -0.005968   -0.065649     0.049900  ... -0.015211  0.031706  -0.011378
    BsmtUnfSF     -0.007940   -0.140759     0.132644  ...  0.034888 -0.041258   0.214479
    TotalBsmtSF   -0.015415   -0.238518     0.392075  ...  0.013196 -0.014969   0.613581
    1stFlrSF       0.010496   -0.251758     0.457181  ...  0.031372 -0.013604   0.605852
    2ndFlrSF       0.005590    0.307886     0.080177  ...  0.035164 -0.028700   0.319334
    LowQualFinSF  -0.044230    0.046474     0.038469  ... -0.022174 -0.028921  -0.025606
    GrLivArea      0.008273    0.074853     0.402797  ...  0.050240 -0.036526   0.708624
    BsmtFullBath   0.002289    0.003491     0.100949  ... -0.025361  0.067049   0.227122
    BsmtHalfBath  -0.020155   -0.002333    -0.007234  ...  0.032873 -0.046524  -0.016844
    FullBath       0.005587    0.131608     0.198769  ...  0.055872 -0.019669   0.560664
    HalfBath       0.006784    0.177354     0.053532  ... -0.009050 -0.010269   0.284108
    BedroomAbvGr   0.037719   -0.023438     0.263170  ...  0.046544 -0.036014   0.168213
    KitchenAbvGr   0.002951    0.281721    -0.006069  ...  0.026589  0.031687  -0.135907
    TotRmsAbvGrd   0.027239    0.040380     0.352096  ...  0.036907 -0.034516   0.533723
    Fireplaces    -0.019772   -0.045569     0.266639  ...  0.046357 -0.024096   0.466929
    GarageYrBlt    0.000072    0.085072     0.070250  ...  0.005337 -0.001014   0.486362
    GarageCars     0.016570   -0.040110     0.285691  ...  0.040522 -0.039117   0.640409
    GarageArea     0.017634   -0.098672     0.344997  ...  0.027974 -0.027378   0.623431
    WoodDeckSF    -0.029643   -0.012579     0.088521  ...  0.021011  0.022270   0.324413
    OpenPorchSF   -0.000477   -0.006100     0.151972  ...  0.071255 -0.057619   0.315856
    EnclosedPorch  0.002889   -0.012037     0.010700  ... -0.028887 -0.009916  -0.128578
    3SsnPorch     -0.046635   -0.043825     0.070029  ...  0.029474  0.018645   0.044584
    ScreenPorch    0.001330   -0.026030     0.041383  ...  0.023217  0.010694   0.111447
    PoolArea       0.057044    0.008283     0.206167  ... -0.033737 -0.059689   0.092404
    MiscVal       -0.006242   -0.007683     0.003368  ... -0.006495  0.004906  -0.021190
    MoSold         0.021172   -0.013585     0.011200  ...  1.000000 -0.145721   0.046432
    YrSold         0.000712   -0.021407     0.007450  ... -0.145721  1.000000  -0.028923
    SalePrice     -0.021917   -0.084284     0.351799  ...  0.046432 -0.028923   1.000000
    
    [38 rows x 38 columns]
    coef and intercept:  [0.21627565 0.41024884] 0.0543428481373919
    cost after log： -3.850369422061899 -4.52343070892457
    best w1 and w2 after GD： 0.10003438525600654 0.30004957896248946

## 核心代码 ##

LiR = linear_model.LinearRegression()
    LiR.fit(X_train, y_train)
    print('coef and intercept: ',LiR.coef_,LiR.intercept_)
    
    def CalCostByW(train_df,slope):
        w1_lists=[];w2_lists=[];cost_lists=[]
        for i in range (30):
            for j in range(30):
                w1= slope*i+0.1
                w2= slope*j+0.3
                w1_lists.append(w1); w2_lists.append(w2)
                cost_lists.append(cost(train_df,train_df.LotFrontage,train_df.GarageArea,w1,w2))
               # print (cost(train_df))
        return w1_lists,w2_lists,cost_lists
    w1_lists,w2_lists,cost_lists=CalCostByW(train_t,0.01)

[ML_LiR_LiR_MSE_GD_Boston]: https://yunyaniu.blog.csdn.net/article/details/110210222
[ML_LiR_LiR_MSE_GD_Boston 1]: https://yunyaniu.blog.csdn.net/article/details/110248933
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70]: /images/20221120/e97a9ff67b65441f92c736af53efcb34.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20201126232516394.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/2020112623260775.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4,size_16,color_FFFFFF,t_70