tick.survival.CoxRegression(penalty='l2', C=1000.0, solver='agd', step=None, tol=1e-05, max_iter=100, verbose=False, warm_start=False, print_every=10, record_every=10, elastic_net_ratio=0.95, random_state=None, blocks_start=None, blocks_length=None)[source]¶Cox regression learner, using the partial Cox likelihood for proportional risks, with many choices of penalization.
Note that this learner does not have predict functions
C : float, default=1e3
Level of penalization
penalty : {‘none’, ‘l1’, ‘l2’, ‘elasticnet’, ‘tv’, ‘binarsity’}, default=’l2’
The penalization to use. Default is ‘l2’, namely Ridge penalization
solver : {‘gd’, ‘agd’}, default=’agd’
The name of the solver to use.
warm_start : bool, default=False
If true, learning will start from the last reached solution
step : float, default=None
Initial step size used for learning. Used when solver is ‘gd’ or ‘agd’.
tol : float, default=1e-5
The tolerance of the solver (iterations stop when the stopping criterion is below it). By default the solver does
max_iteriterations
max_iter : int, default=100
Maximum number of iterations of the solver
verbose : bool, default=True
If
True, we verbose things, otherwise the solver does not print anything (but records information in history anyway)
print_every : int, default=10
Print history information when
n_iter(iteration number) is a multiple ofprint_every
record_every : int, default=10
Record history information when
n_iter(iteration number) is a multiple ofrecord_every
coeffs : np.array, shape=(n_features,)
The learned coefficients of the model