Quick Start

Workflow

flowchart LR
    A["Prepare X, y"] --> B["Split train/test"]
    B --> C["Create estimator"]
    C --> D["fit"]
    D --> E["predict"]
    E --> F["evaluate"]

Regression

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from kfc_procedure import KFCRegressor

X, y = make_regression(n_samples=300, n_features=8, noise=0.2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

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

Classification

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from kfc_procedure import KFCClassifier

X, y = make_classification(n_samples=300, n_features=8, n_informative=5, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)

clf = KFCClassifier(
    divergences=["euclidean"],
    local_model="decision_tree_classifier",
    combiner="majority_vote",
    n_clusters=2,
    random_state=42,
)
clf.fit(X_train, y_train)
print("Accuracy:", accuracy_score(y_test, clf.predict(X_test)))

Main parameters

Parameter	Meaning	Example
divergences	list of Bregman divergences	["euclidean"]
local_model	supervised model fitted per cluster	"linear_regression"
combiner	final aggregation strategy	"mean"
n_clusters	number of clusters per divergence	3
random_state	reproducibility seed	42

Divergence domains

gkl and is require positive data. logistic requires values in (0, 1).