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

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

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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
 

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

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)