`hazardous.metrics`.integrated_brier_score_survival#

hazardous.metrics.integrated_brier_score_survival(y_train, y_test, y_pred, times)#

Compute the Brier score integrated over the observed time range.

\[\mathrm{IBS} = \frac{1}{t_{max} - t_{min}} \int^{t_{max}}_{t_{min}} \mathrm{BS}(u) du\]

Note that this assumes independence between censoring and the covariates. When this assumption is violated, the IPCW weights are biased and the Brier score is not a proper scoring rule anymore. See [Gerds2006] for a study of this bias.

Parameters:

y_trainrecord-array, dictionnary or dataframe of shape (n_samples, 2): The target, consisting in the ‘event’ and ‘duration’ columns. If the ‘event’ column holds more than 1 event types, they are automatically collapsed to a single event type to compute the Brier score of the “any-event” survival function estimate. This is only used to estimate the IPCW values to adjust for censoring in the evaluation data.
y_testrecord-array, dictionnary or dataframe of shape (n_samples, 2): The ground truth, consisting in the ‘event’ and ‘duration’ columns. The same remark applies as for y_train with respect to the ‘event’ column.
y_predarray-like of shape (n_samples, n_times): Survival probability estimates predicted at times.
timesarray-like of shape (n_times): Times at which the survival probabilities y_pred has been estimated and for which we compute the Brier score.

Returns:

ibsfloat

hazardous.metrics.integrated_brier_score_survival#

`hazardous.metrics`.integrated_brier_score_survival#