1.15. Isotonic regression¶
The class IsotonicRegression
fits a non-decreasing real function to
1-dimensional data. It solves the following problem:
minimize
subject to
whenever
,
where the weights are strictly positive, and both
X
and y
are
arbitrary real quantities.
The increasing
parameter changes the constraint to
whenever
. Setting it to
‘auto’ will automatically choose the constraint based on Spearman’s rank
correlation coefficient.
IsotonicRegression
produces a series of predictions
for the training data which are the closest to the targets
in terms of mean squared error. These predictions are interpolated
for predicting to unseen data. The predictions of
IsotonicRegression
thus form a function that is piecewise linear: