COBRA Usage

The package includes COBRA-style estimators under kfc_procedure.cobra.

Estimator	Task	Summary
GradientCOBRA	Regression	prediction-space kernel aggregation with optimized bandwidth
MixCOBRARegressor	Regression	mixes input-space and prediction-space distances
CombinedClassifier	Classification	kernel-weighted voting in prediction space
SuperLearner	Regression/stacking	base learners plus meta learners

GradientCOBRA

from kfc_procedure.cobra import GradientCOBRA

model = GradientCOBRA(kernel="rbf", distance="euclidean", max_iter=100, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

MixCOBRARegressor

from kfc_procedure.cobra import MixCOBRARegressor

model = MixCOBRARegressor(distance="euclidean", kernel="rbf", max_iter=100, random_state=42)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

CombinedClassifier

from kfc_procedure.cobra import CombinedClassifier

clf = CombinedClassifier(distance="hamming", kernel="rbf", max_iter=100, random_state=42)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)