skglm.experimental.SmoothQuantileRegressor

class skglm.experimental.SmoothQuantileRegressor(quantile=0.5, alpha=0.1, delta_init=1.0, delta_final=0.001, n_deltas=10, max_iter=1000, tol=1e-06, verbose=False, fit_intercept=True)

Quantile regression with progressive smoothing.

__init__(quantile=0.5, alpha=0.1, delta_init=1.0, delta_final=0.001, n_deltas=10, max_iter=1000, tol=1e-06, verbose=False, fit_intercept=True)

Methods

__init__([quantile, alpha, delta_init, ...])
fit(X, y)	Fit using progressive smoothing: delta_init --> delta_final.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict using the fitted model.
score(X, y[, sample_weight])	Return coefficient of determination on test data.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.