如何用scipy的curve_fit调整线性回归？

Question

我最近熟练使用Python / scipy curve_fit来执行线性回归。但是，对于高阶多项式，我的数据有时会过度拟合。

如何添加正则化以减少过度拟合？

Answer 1

我想知道lasso penalization是否适合你：

# the high order items can be integrated into X (such as x1^2,x1*x2), and change it into a linear regression problem again
lasso.fit(X, y) 
# the selection range of lambda can be determined by yourself.
LassoCV(lambda=array([ 2, 1,9, ..., 0.2 , 0.1]),  
copy_X=True, cv=None, eps=0.001, fit_intercept=True, max_iter=1000,
n_alphas=100, normalize=False, precompute=’auto’, tol=0.0001,
verbose=False)

应该在交叉验证期间选择最佳lambda。