statsmodels.regression.linear_model.GLS.fit_regularized¶
-
GLS.fit_regularized(method=
'elastic_net', alpha=0.0, L1_wt=1.0, start_params=None, profile_scale=False, refit=False, **kwargs)[source]¶ Return a regularized fit to a linear regression model.
- Parameters:¶
- method
str Either ‘elastic_net’ or ‘sqrt_lasso’.
- alphascalar or array_like
The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as params, and contains a penalty weight for each coefficient.
- L1_wtscalar
The fraction of the penalty given to the L1 penalty term. Must be between 0 and 1 (inclusive). If 0, the fit is a ridge fit, if 1 it is a lasso fit.
- start_paramsarray_like
Starting values for
params.- profile_scalebool
If True the penalized fit is computed using the profile (concentrated) log-likelihood for the Gaussian model. Otherwise the fit uses the residual sum of squares.
- refitbool
If True, the model is refit using only the variables that have non-zero coefficients in the regularized fit. The refitted model is not regularized.
- **kwargs
Additional keyword arguments that contain information used when constructing a model using the formula interface.
- method
- Returns:¶
statsmodels.base.elastic_net.RegularizedResultsThe regularized results.
Notes
The elastic net uses a combination of L1 and L2 penalties. The implementation closely follows the glmnet package in R.
The function that is minimized is:
\[0.5*RSS/n + alpha*((1-L1\_wt)*|params|_2^2/2 + L1\_wt*|params|_1)\]where RSS is the usual regression sum of squares, n is the sample size, and \(|*|_1\) and \(|*|_2\) are the L1 and L2 norms.
For WLS and GLS, the RSS is calculated using the whitened endog and exog data.
Post-estimation results are based on the same data used to select variables, hence may be subject to overfitting biases.
The elastic_net method uses the following keyword arguments:
- maxiterint
Maximum number of iterations
- cnvrg_tolfloat
Convergence threshold for line searches
- zero_tolfloat
Coefficients below this threshold are treated as zero.
The square root lasso approach is a variation of the Lasso that is largely self-tuning (the optimal tuning parameter does not depend on the standard deviation of the regression errors). If the errors are Gaussian, the tuning parameter can be taken to be
alpha = 1.1 * np.sqrt(n) * norm.ppf(1 - 0.05 / (2 * p))
where n is the sample size and p is the number of predictors.
The square root lasso uses the following keyword arguments:
- zero_tolfloat
Coefficients below this threshold are treated as zero.
The cvxopt module is required to estimate model using the square root lasso.
References