ENH: improve jackknife docstring

Author: qroa

mapie_v1/regression.py

@@ -567,16 +567,19 @@ class JackknifeAfterBootstrapRegressor:
         be provided.
 
     method : str, default="plus"
-        The method used for jackknife-after-bootstrap prediction. Options are:
-        - "base": Based on the conformity scores from each bootstrap sample.
-        - "plus": Based on the conformity scores from each bootstrap sample and
-        the testing prediction.
-        - "minmax": Based on the minimum and maximum conformity scores from
-        each bootstrap sample.
+        Jackknife-after-bootstrap method for prediction intervals:
+        - "plus": Combines bootstrap conformity scores with test predictions.
+        - "minmax": Minimum and maximum conformity scores from bootstrap samples.
 
-    n_bootstraps : int, default=100
-        The number of bootstrap resamples to generate for the
-        jackknife-after-bootstrap procedure.
+        Note: The "base" method is not authorized and should not be used.
+
+    resampling : Union[int, Subsample], default=30
+        Number of bootstrap resamples or an instance of `Subsample` for
+        custom resampling strategy.
+
+    aggregation_method : str, default="mean"
+        Aggregation method for predictions across bootstrap samples.
+        Options: ["mean", "median"].
 
     n_jobs : Optional[int], default=None
         The number of jobs to run in parallel when applicable.
@@ -763,14 +766,18 @@ def predict_set(
         X : ArrayLike
             Test data for prediction intervals.
 
+        minimize_interval_width : bool, default=False
+            If True, minimizes the width of prediction intervals while
+            maintaining coverage.
+
         allow_infinite_bounds : bool, default=False
             If True, allows intervals to include infinite bounds
             if required for coverage.
 
         Returns
         -------
         NDArray
-            Prediction intervals of shape `(n_samples, 2)`,
+            Prediction intervals of shape (n_samples, 2),
             with lower and upper bounds for each sample.
         """
         _, intervals = self._mapie_regressor.predict(