Regression

Use KFCRegressor for continuous target variables.

from kfc_procedure import KFCRegressor

model = KFCRegressor(
    divergences=["euclidean"],
    local_model="linear_regression",
    combiner="mean",
    n_clusters=3,
    random_state=42,
)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Regression combiners

Combiner	Behavior
mean	arithmetic mean across prediction columns
weighted_mean	OLS learns weights over prediction columns
stacking_regressor	meta-regressor over prediction matrix
gradientcobra	COBRA kernel-weighted aggregation
mixcobra	COBRA aggregation using input and prediction spaces

Metrics

from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
print(mean_absolute_error(y_test, y_pred))
print(mean_squared_error(y_test, y_pred))
print(r2_score(y_test, y_pred))