我很難讓 的線性回歸中的加權(quán)數(shù)組影響輸出。sklearn下面是一個沒有權(quán)重的示例。import numpy as npimport seaborn as snsfrom sklearn import linear_modelx = np.arange(0,100.)y = (x**2.0)xr = np.array(x).reshape(-1, 1)yr = np.array(y).reshape(-1, 1)regr = linear_model.LinearRegression()regr.fit(xr, yr)y_pred = regr.predict(xr)sns.scatterplot(x=x, y = y)sns.lineplot(x=x, y = y_pred.T[0].tolist())現(xiàn)在，在添加權(quán)重時，我得到了相同的最佳配合線。我預(yù)計會看到回歸有利于曲線的更陡峭部分。我做錯了什么？w = [p**2 for p in x.reshape(-1)]wregr = linear_model.LinearRegression()wregr.fit(xr,yr, sample_weight=w)yw_pred = regr.predict(xr)wregr = linear_model.LinearRegression(fit_intercept=True)wregr.fit(xr,yr, sample_weight=w)yw_pred = regr.predict(xr)sns.scatterplot(x=x, y = y)                      #plot curvesns.lineplot(x=x, y = y_pred.T[0].tolist())      #plot non-weighted best fit linesns.lineplot(x=x, y = yw_pred.T[0].tolist())     #plot weighted best fit line