IsotonicRegression

#include <Skigen/Isotonic>

template <typename Scalar = double>
class Skigen::IsotonicRegression(y_min=std::nullopt, y_max=std::nullopt, increasing=`IsotonicIncreasing::True`, out_of_bounds=`OutOfBounds::Nan`)

Univariate monotonic regression with the Pool-Adjacent-Violators algorithm.

Mirrors sklearn.isotonic.IsotonicRegression.

Solves the weighted constrained least-squares problem

$$\min_{\hat{y}} \; \sum_i w_i (y_i - \hat{y}_i)^2 \quad \text{subject to} \quad \hat{y}_i \le \hat{y}_{i+1} \;\;\forall i$$

(the constraint reverses for increasing = False). The fitted function is piecewise-linear; predictions interpolate between adjacent thresholds.

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Attributes:

• y_min : std::optional< Scalar >

• y_max : std::optional< Scalar >

• increasing_param : IsotonicIncreasing

• out_of_bounds : OutOfBounds

• X_min : Scalar

• X_max : Scalar

• X_thresholds : VectorType

• y_thresholds : VectorType

• increasing_resolved : bool Effective monotonicity direction after fitting.

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Methods

fit(X, y)

Fit the isotonic regression on a 1-D feature.

X must have shape (n_samples, 1). For multi-column X this method throws std::invalid_argument (matching sklearn).

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predict(X)

Predict using the fitted piecewise-linear isotonic function.

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transform(X)

Convenience: same as predict for sklearn API parity.

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score(X, y)

R^2 score on (X, y).

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