Implementation:DistrictDataLabs Yellowbrick ContribEstimatorWrapper

Knowledge Sources	DistrictDataLabs_Yellowbrick
Domains	Model_Evaluation, Utilities
Last Updated	2026-02-08 05:00 GMT

Overview

Wrapper utilities for adapting third-party estimators that implement the sklearn API but do not subclass BaseEstimator for use with Yellowbrick visualizers.

Description

The contrib.wrapper module provides the ContribEstimator proxy class and convenience functions wrap, classifier, regressor, and clusterer. These wrap any object implementing the sklearn API (fit/predict/score) so Yellowbrick can correctly identify it as a classifier, regressor, or clusterer.

Usage

Import wrap or the type-specific functions when using third-party ML libraries (e.g., XGBoost, LightGBM without sklearn wrappers) with Yellowbrick visualizers.

Code Reference

Source Location

• Repository: DistrictDataLabs_Yellowbrick
• File: yellowbrick/contrib/wrapper.py
• Lines: 1-134

Signature

def wrap(estimator, estimator_type=None):
    """Wraps a third-party estimator for Yellowbrick compatibility."""

def classifier(estimator):
    """Wraps estimator as a classifier."""

def regressor(estimator):
    """Wraps estimator as a regressor."""

def clusterer(estimator):
    """Wraps estimator as a clusterer."""

class ContribEstimator:
    def __init__(self, estimator, estimator_type=None):
        """Proxy class for third-party estimators."""

    def __getattr__(self, attr):
        """Proxies attribute access to wrapped estimator."""

Import

from yellowbrick.contrib.wrapper import wrap, classifier, regressor, clusterer

I/O Contract

Inputs

Name	Type	Required	Description
estimator	object	Yes	Third-party estimator with fit/predict/score
estimator_type	str	No	"classifier", "regressor", or "clusterer"

Outputs

Name	Type	Description
wrapped	ContribEstimator	Yellowbrick-compatible proxy estimator

Usage Examples

from yellowbrick.contrib.wrapper import classifier
from yellowbrick.classifier import ROCAUC

# Wrap a third-party classifier
import xgboost as xgb
model = classifier(xgb.XGBClassifier())

viz = ROCAUC(model)
viz.fit(X_train, y_train)
viz.score(X_test, y_test)
viz.show()

Related Pages

• Environment:DistrictDataLabs_Yellowbrick_Python_Scikit_Learn_Environment