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:

min sum w[i] (y[i] - y_[i]) ** 2

subject to y_min = y_[1] <= y_[2] ... <= y_[n] = y_max

where:

• y[i] are inputs (real numbers)

• y_[i] are fitted

• w[i] are optional strictly positive weights (default to 1.0)

Read more in the User Guide.

Parameters

yiterable of floats

The data.

sample_weightiterable of floats, optional, default: None

Weights on each point of the regression. If None, weight is set to 1 (equal weights).

y_minoptional, default: None

If not None, set the lowest value of the fit to y_min.

y_maxoptional, default: None

If not None, set the highest value of the fit to y_max.

increasingboolean, optional, default: True

Whether to compute y_ is increasing (if set to True) or decreasing (if set to False)

Returns

y_list of floats

Isotonic fit of y.

References

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