statsmodels.duration.hazard_regression.PHReg.fit_regularized
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PHReg.fit_regularized(method='coord_descent', maxiter=100, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-07, zero_tol=1e-08, **kwargs)
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Return a regularized fit to a linear regression model.
Parameters: method : :
Only the coordinate descent algorithm is implemented.
maxiter : integer
The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables).
alpha : scalar 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_wt : scalar
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_params : array-like
Starting values for
params
.cnvrg_tol : scalar
If
params
changes by less than this amount (in sup-norm) in once iteration cycle, the algorithm terminates with convergence.zero_tol : scalar
Any estimated coefficient smaller than this value is replaced with zero.
Returns: A PHregResults object, of the same type returned by `fit`. :
Notes
The penalty is the?elastic net? penalty, which is a convex combination of L1 and L2 penalties.
The function that is minimized is: ..math:
-loglike/n + alpha*((1-L1_wt)*|params|_2^2/2 + L1_wt*|params|_1)
where and are the L1 and L2 norms.
Post-estimation results are based on the same data used to select variables, hence may be subject to overfitting biases.
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