sklearn.isotonic.isotonic_regression¶
- sklearn.isotonic.isotonic_regression(y, sample_weight=None, y_min=None, y_max=None, weight=None, increasing=True)¶
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)
Parameters : y : iterable of floating-point values
The data.
sample_weight : iterable of floating-point values, optional, default: None
Weights on each point of the regression. If None, weight is set to 1 (equal weights).
y_min : optional, default: None
If not None, set the lowest value of the fit to y_min.
y_max : optional, default: None
If not None, set the highest value of the fit to y_max.
increasing : boolean, optional, default: True
Whether to compute y_ is increasing (if set to True) or decreasing (if set to False)
Returns : `y_` : list of floating-point values
Isotonic fit of y.
References
“Active set algorithms for isotonic regression; A unifying framework” by Michael J. Best and Nilotpal Chakravarti, section 3.