fix k_fold_cross_validation documentation
It looked outdated
@@ -715,22 +715,18 @@ def k_fold_cross_validation(
a Pandas DataFrame with necessary columns `duration_col` and (optional) `event_col`, plus
other covariates. `duration_col` refers to the lifetimes of the subjects. `event_col`
refers to whether the 'death' events was observed: 1 if observed, 0 else (censored).
- duration_col: (n,) array
- the column in DataFrame that contains the subjects lifetimes.
- event_col: (n,) array
- the column in DataFrame that contains the subject's death observation. If left
- as None, assumes all individuals are non-censored.
+ duration_col: string
+ the name of the column in DataFrame that contains the subjects'
+ lifetimes.
+ event_col: string, optional
+ the name of the column in DataFrame that contains the subjects' death
+ observation. If left as None, assume all individuals are uncensored.
k: int
the number of folds to perform. n/k data will be withheld for testing on.
- evaluation_measure: function
- a function that accepts either (event_times, predicted_event_times),
- or (event_times, predicted_event_times, event_observed).
- Default: statistics.concordance_index: (C-index)
- predictor: string
- a string that matches a prediction method on the fitter instances.
- For example, ``predict_expectation`` or ``predict_percentile``.
- Default is "predict_expectation"
- The interface for the method is: ``predict(self, data, **optional_kwargs)``
+ scoring_method: str
+ one of {'log_likelihood', 'concordance_index'}
+ log_likelihood: returns the average unpenalized partial log-likelihood.
+ concordance_index: returns the concordance-index
fitter_kwargs:
keyword args to pass into fitter.fit method.
