编辑：根据要求删除TransformedTargetRegressor和添加更多信息。
Edit2：有 18K 行的关系不成立。对不起:(。删除这些行并根据@Ben Reiniger 的建议，我使用了LinearRegression，指标看起来更合理。新指标粘贴在下面。
原始问题：
鉴于totalRevenueand costOfRevenue，我试图预测grossProfit。鉴于这是一个简单的公式totalRevenue - costOfRevenue = grossProfit，我期待下面的代码可以工作。是超参数优化的问题还是我错过了一些数据清理。我已经尝试了所有的缩放器和其他回归，sklearn但我没有看到任何大的区别。
# X(107002 rows × 2 columns)
+--------------+---------------+
| totalRevenue | costOfRevenue |
+--------------+---------------+
| 2.256510e+05 | 2.333100e+04 |
| 1.183960e+05 | 2.857000e+04 |
| 2.500000e+05 | 1.693000e+05 |
| 1.750000e+05 | 8.307500e+04 |
| 3.905000e+09 | 1.240000e+09 |
+--------------+---------------+
# y
+--------------+
| 2.023200e+05 |
| 8.982600e+04 |
| 8.070000e+04 |
| 9.192500e+04 |
| 2.665000e+09 |
+--------------+
Name: grossProfit, Length: 107002, dtype: float64
# Training
import numpy as np
import sklearn
from sklearn.compose import TransformedTargetRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=13)
x_scaler = StandardScaler()
pipe_l = Pipeline([
('scaler', x_scaler),
('regressor', Ridge())
])
regr = pipe_l
regr.fit(X_train, y_train)
y_pred = regr.predict(X_test)
print('R2 score: {0:.2f}'.format(sklearn.metrics.r2_score(y_test, y_pred)))
print('Mean Absolute Error:', sklearn.metrics.mean_absolute_error(y_test, y_pred))
print('Mean Squared Error:', sklearn.metrics.mean_squared_error(y_test, y_pred))
print('Root Mean Squared Error:', np.sqrt(sklearn.metrics.mean_squared_error(y_test, y_pred)))
print("Scaler Mean:",x_scaler.mean_)
print("Scaler Var:", x_scaler.var_)
print("Estimator Coefficient:",regr.steps[1][1].coef_)
训练后上述指标的输出（旧指标有 18k 行，未确认关系）
R2 score: 0.69
Mean Absolute Error: 37216342513.01034
Mean Squared Error: 7.601569571667974e+23
Root Mean Squared Error: 871869805169.7842
Scaler Mean: [1.26326695e+13 2.14785735e+14]
Scaler Var: [1.24609190e+31 2.04306993e+32]
Estimator Coefficient: [1.16354874e+15 2.59046205e+09]
Ridge（删除 18k 坏行后）
R2 score: 1.00
Mean Absolute Error: 15659273.260432156
Mean Squared Error: 8.539990125466045e+16
Root Mean Squared Error: 292232614.97420245
Scaler Mean: [1.57566809e+11 9.62274405e+10]
Scaler Var: [1.20924187e+25 5.95764210e+24]
Estimator Coefficient: [ 3.47663586e+12 -2.44005648e+12]
线性回归（删除 18K 行后）
R2 score: 1.00
Mean Absolute Error: 0.00017393178061611583
Mean Squared Error: 4.68109129068828e-06
Root Mean Squared Error: 0.0021635829752261132
Scaler Mean: [1.57566809e+11 9.62274405e+10]
Scaler Var: [1.20924187e+25 5.95764210e+24]
Estimator Coefficient: [ 3.47741552e+12 -2.44082816e+12]