End-to-End XGBoost Regression Pipeline with Scikit-Learn

Regression is a technique in statistics and machine learning, in which the value of an independent variable is predicted by its relationship with other variables.

Frameworks like Scikit-Learn and XGBoost make it easier than ever to perform regression with a wide variety of models - one of the recently well-adopted one being Gradient Boosting and XGBoost's variant, Extreme Gradient Boosting models.

XGBoost works well with Scikit-Learn, has a similar API, and can in most cases be used just like a Scikit-Learn model - so it's natural to be able to build pipelines with both libraries.

With Scikit-Learn pipelines, you can create an end-to-end pipeline in as little as 4 lines of code: load a dataset, perform feature scaling, and then feed the data into a regression model:

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.pipeline import Pipeline

from xgboost import XGBRegressor

X, y = datasets.fetch_california_housing(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)

pipeline = Pipeline([('scaler', MinMaxScaler()), ('regressor', XGBRegressor())])
pipeline.fit(X_train, y_train)

r2 = pipeline.score(X_test, y_test)
print(f"XGBoost regressor r2: {r2}")
# XGBoost regressor r2: 0.8323581845230325

Alternatively, you can separate the steps outside of the pipeline, which is a bit more verbose, yet more flexible:

X, y = datasets.fetch_california_housing(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)

scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

xbg_reg = XGBRegressor().fit(X_train_scaled, y_train)

r2 = xbg_reg.score(X_test_scaled, y_test)
print(f"XGBoost Regressor: {r2}")

# XGBoost regressor r2: 0.8294001231626023
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David LandupAuthor

Entrepreneur, Software and Machine Learning Engineer, with a deep fascination towards the application of Computation and Deep Learning in Life Sciences (Bioinformatics, Drug Discovery, Genomics), Neuroscience (Computational Neuroscience), robotics and BCIs.

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