你好，我正在尝试使用scikit-learn来拟合一小组数据。
import numpy as npfrom sklearn import linear_model, model_selectionX = np.array([[86.5999984741211, 9.10000038146973, 14.3000001907349,1], [66.9000015258789, 17.3999996185303, 11.5,1], [66.3000030517578, 20 , 10.6999998092651,1], [78.6999969482422, 15.3999996185303, 12.1000003814697,1], [76.1999969482422, 18.2000007629395, 12.5,1], [84.4000015258789, 9.89999961853027, 12.1000003814697,1], [79.1999969482422, 8.5 , 10.1000003814697,1], [77.5 , 10.1999998092651, 11.3999996185303,1], [74.4000015258789, 17.7999992370605, 10.6000003814697,1], [870.9000015258789, 13.5 , 13,1], [80.0999984741211, 8 , 9.10000038146973,1], [80.0999984741211, 10.3000001907349, 9,1], [79.6999969482422, 13.1000003814697, 9.5,1], [76.1999969482422, 13.6000003814697, 11.5,1], [75.5999984741211, 12.1999998092651, 10.8000001907349,1], [81.3000030517578, 13.1000003814697, 9.89999961853027,1], [64.5999984741211, 20.3999996185303, 10.6000003814697,1], [68.3000030517578, 26.3999996185303, 14.8999996185303,1], [80 , 10.6999998092651, 10.8999996185303,1], [78.4000015258789, 9.69999980926514, 12,1], [78.8000030517578, 10.6999998092651, 10.6000003814697,1], [76.8000030517578, 15.3999996185303, 13,1], [82.4000015258789, 11.6000003814697, 9.89999961853027,1], [73.9000015258789, 16.1000003814697, 10.8999996185303,1], [64.3000030517578, 24.7000007629395, 14.6999998092651,1], [81 , 14.8999996185303, 10.8000001907349,1], [70 , 14.3999996185303, 11.1000003814697,1], [76.6999969482422, 11.1999998092651, 8.39999961853027,1], [81.8000030517578, 10.3000001907349, 9.39999961853027,1], [82.1999969482422, 9.89999961853027, 9.19999980926514,1], [76.6999969482422, 10.8999996185303, 9.60000038146973,1], [75.0999984741211, 17.3999996185303, 13.8000001907349,1], [78.8000030517578, 9.80000019073486, 12.3999996185303,1], [74.8000030517578, 16.3999996185303, 12.6999998092651,1], [75.6999969482422, 13 , 11.3999996185303,1], [74.5999984741211, 19.8999996185303, 11.1000003814697,1], [81.5 , 11.8000001907349, 11.3000001907349,1], [74.6999969482422, 13.1999998092651, 9.60000038146973,1], [72 , 11.1999998092651, 10.8000001907349,1], [68.3000030517578, 18.7000007629395, 12.3000001907349,1], [77.0999984741211, 14.1999998092651, 9.39999961853027,1], [67.0999984741211, 19.6000003814697, 11.1999998092651,1], [72.0999984741211, 17.3999996185303, 11.8000001907349,1], [85.0999984741211, 10.6999998092651, 10,1], [75.1999969482422, 9.69999980926514, 10.3000001907349,1], [80.8000030517578, 10 , 11,1], [83.8000030517578, 12.1000003814697, 11.6999998092651,1], [78.5999984741211, 12.6000003814697, 10.3999996185303,1], [66 , 22.2000007629395, 9.39999961853027,1], [83 , 13.3000001907349, 10.8000001907349,1], [73.0999984741211, 26.3999996185303, 22.1000003814697,1]])y = np.array([761, 780, 593, 715, 1078, 567, 456, 686, 1206, 723, 261, 326, 282, 960, 489, 496, 463, 1062, 805, 998, 126, 792, 327, 744, 434, 178, 679, 82, 339, 138, 627, 930, 875, 1074, 504, 635, 503, 418, 402, 1023, 208, 766, 762, 301, 372, 114, 515, 264, 208, 286, 2922])model = linear_model.SGDRegressor(max_iter=0x7FFFFFFF, tol=1e-12, learning_rate="constant", eta0=.1, shuffle=False)"""model = linear_model.Lasso(max_iter=0x7FFFFFF,tol=1e-12)"""model.fit(X,y)print(model.coef_)print (model.score(X,y))"""for i in range(0,len(X)): print (np.dot(X[i],model.coef_))"""Ridge/Lasso/ElasticNet 在一定程度上可以拟合（得分约为0.7），但即使我设置了超高的迭代次数和极低的容忍度值，SGDRegressor 仍然无法很好地拟合这些数据。
调整 max_iter 或 tol 对结果没有任何影响，我总是得到非常大的系数。
回答：
在应用梯度下降技术之前，你需要确保你的特征已经进行了缩放。看看你的 X 数据，这样应该能解决问题。
from sklearn.preprocessing import StandardScalerX_scaled = StandardScaler().fit_transform(X)