SGDRegressor

#include <Skigen/LinearModel>

template <typename Scalar = double>
class Skigen::SGDRegressor(alpha=1e-4, max_iter=1000, tol=1e-3, eta0=0.01, random_state=42)

Linear regressor fitted by minimizing a regularized empirical loss with SGD.

SGDRegressor implements regularized linear regression with stochastic gradient descent (squared error loss, L2 penalty).

Mirrors sklearn.linear_model.SGDRegressor.

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Parameters:

• alpha : Scalar, default=1e-4 Regularization constant (Scalar, default 1e-4).

• max_iter : int, default=1000 Maximum number of epochs (int, default 1000).

• tol : Scalar, default=1e-3 Stopping tolerance (Scalar, default 1e-3).

• eta0 : Scalar, default=0.01 Initial learning rate (Scalar, default 0.01).

• random_state : unsigned int, default=42 RNG seed (unsigned int, default 42).

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Attributes:

• is_fitted : bool Whether the estimator has been fitted.

• coef : RowVectorType Parameter vector $w$ (1 × n_features).

• intercept : Scalar Independent term in the decision function.

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Methods

fit(X, y)

Fit the linear model with SGD.

Parameters:

• X : MatrixType Training matrix of shape (n_samples, n_features).

• y : VectorType Target vector of shape (n_samples,).

Returns:

• result : SGDRegressor Reference to the fitted estimator (*this).

Throws:

• std::invalid_argument — if X and y have inconsistent lengths.

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predict(X)

Predict using the linear model.

Parameters:

• X : MatrixType Sample matrix of shape (n_samples, n_features).

Returns:

• result : VectorType Predicted values of shape (n_samples,).

Throws:

• std::runtime_error — if the model has not been fitted.

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score(X, y)

Return the $R^2$ coefficient of determination on test data.

Parameters:

• X : MatrixType Test samples of shape (n_samples, n_features).

• y : VectorType True values of shape (n_samples,).

Returns:

• result : Scalar $R^2$ score.

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Example

Skigen::SGDRegressor<double> regressor;
regressor.fit(split_reg.X_train, split_reg.y_train);

std::cout << "=== SGD Regressor ===\n";
std::cout << "R²:   " << regressor.score(split_reg.X_test, split_reg.y_test) << "\n";
std::cout << "Coef: " << regressor.coef() << "\n";

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