`sklearn.isotonic`.isotonic_regression#

sklearn.isotonic.isotonic_regression(y, *, sample_weight=None, y_min=None, y_max=None, increasing=True)[source]#

Solve the isotonic regression model.

Read more in the User Guide.

Parameters:

yarray-like of shape (n_samples,): The data.
sample_weightarray-like of shape (n_samples,), default=None: Weights on each point of the regression. If None, weight is set to 1 (equal weights).
y_minfloat, default=None: Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf.
y_maxfloat, default=None: Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf.
increasingbool, default=True: Whether to compute y_ is increasing (if set to True) or decreasing (if set to False).

Returns:

y_ndarray of shape (n_samples,): Isotonic fit of y.

References

“Active set algorithms for isotonic regression; A unifying framework” by Michael J. Best and Nilotpal Chakravarti, section 3.

Examples

>>> from sklearn.isotonic import isotonic_regression
>>> isotonic_regression([5, 3, 1, 2, 8, 10, 7, 9, 6, 4])
array([2.75   , 2.75   , 2.75   , 2.75   , 7.33...,
       7.33..., 7.33..., 7.33..., 7.33..., 7.33...])

sklearn.isotonic.isotonic_regression#

`sklearn.isotonic`.isotonic_regression#