我刚刚注意到 Sci-Kit Learn 的线性回归算法将一些不同的结果加载到 Pandas 数据帧中，而不是仅在原始状态下使用它们。
我不明白为什么会这样。
考虑以下线性回归示例：
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
boston = load_boston()
X1 = pd.DataFrame(boston.data)
X1.columns = boston.feature_names
X2 = boston.data
y2 = boston.target
y1 = boston.target
lreg = LinearRegression()
X1 = (X1 - X1.mean()) / X1.std()
X2 = (X2 - X2.mean()) / X2.std()
结果模型给出了相同的 R^2 值和预测值，但系数和截距的结果却大不相同。
展示：
intcpt1 = lreg.fit(X1, y1).intercept_
intcpt2 = lreg.fit(X2, y2).intercept_
f"Intercept for model with dataframe: {intcpt1}, model with numpy array: {intcpt2}"
给出：
'Intercept for model with dataframe: 22.53280632411069, model with numpay array: -941.8009906279219'
同样，系数也大不相同：
coef1 = lreg.fit(X1, y1).coef_[:3]
coef2 = lreg.fit(X2, y2).coef_[:3]
f"First the coeffs for model with dataframe: {coef1}, modely with numpy array: {coef2}"
这使：
'First the coeffs for model with dataframe: [-0.92906457 1.08263896 0.14103943], modely with numpy array: [-15.67844685 6.73818665 2.98419849]'
但是得分和预测是一样的：
score1 = lreg.fit(X1, y1).score(X1, y1)
score2 = lreg.fit(X2, y2).score(X2, y2)
f"Score for model with dataframe: {score1}, model with numpy array: {score2}"
产量：
'Score for model with dataframe: 0.7406426641094094, model with numpy array: 0.7406426641094073'
同样对于系数：
pred1 = lreg.fit(X1, y1).predict(X1)[:3]
pred2 = lreg.fit(X2, y2).predict(X2)[:3]
f"First 3 predictions with dataframe: {pred1}, with numpy array: {pred2}"
提供：
'First 3 predictions with dataframe: [30.00384338 25.02556238 30.56759672], with numpy array: [30.00384338 25.02556238 30.56759672]'
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