您好，我正在尝试使用scilearn拟合少量数据。
import numpy as np
from sklearn import linear_model, model_selection
X = 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在一定程度上适合（〜0.7分数），但是即使我设置了超高迭代和超低tol值，SGDRegressor甚至无法接近这些值。
调整max_iter或tol对结果没有任何影响，我一直在获得巨大的系数。
最佳答案
在应用梯度下降技术之前，您需要确保要素已缩放。查看您的X，这应该可以解决问题。
from sklearn.preprocessing import StandardScaler
X_scaled = StandardScaler().fit_transform(X)
关于python - Scikit-SGDRegressor不适合，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/50551428/