DOC fix default values in isotonic module
@@ -91,10 +91,10 @@ def isotonic_regression(y, sample_weight=None, y_min=None, y_max=None,
Parameters
----------
- y : iterable of floats
+ y : array-like of shape (n_samples,)
The data.
- sample_weight : iterable of floats, optional, default: None
+ sample_weight : array-like of shape (n_samples,), default=None
Weights on each point of the regression.
If None, weight is set to 1 (equal weights).
@@ -160,13 +160,13 @@ class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):
Parameters
----------
- y_min : optional, default: None
+ y_min : float, default=None
If not None, set the lowest value of the fit to y_min.
- y_max : optional, default: None
+ y_max : float, default=None
If not None, set the highest value of the fit to y_max.
- increasing : boolean or string, optional, default: True
+ increasing : bool or string, default=True
If boolean, whether or not to fit the isotonic regression with y
increasing or decreasing.
@@ -174,7 +174,7 @@ class IsotonicRegression(RegressorMixin, TransformerMixin, BaseEstimator):
increase or decrease based on the Spearman correlation estimate's
sign.
- out_of_bounds : string, optional, default: "nan"
+ out_of_bounds : str, default="nan"
The ``out_of_bounds`` parameter handles how x-values outside of the
training domain are handled. When set to "nan", predicted y-values
will be NaN. When set to "clip", predicted y-values will be
