— Technology we master

XGBoost

The reference gradient-boosting library for tabular data.

XGBoost trains ensembles of decision trees with regularized gradient boosting, handles missing values natively, and parallelizes on CPU with GPU acceleration. scikit-learn compatible and available for Python, R and the JVM, it's our default choice when you need the best possible score on tabular data at a reasonable training cost.

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[ Why XGBoost ]

What XGBoost brings to your project.

Typical use cases: Credit scoring, churn, fraud detection and demand forecasting.

01
State of the art on tabular data: scoring, churn, forecasting.
02
Regularized (L1/L2) boosting and native missing-value handling.
03
Parallel training, GPU acceleration, large datasets.
04
Python, R and JVM APIs, compatible with scikit-learn pipelines and validation.

[ Team ]

Entrust your project
to our experts.

Entrust your project to our
experts.

Entrust your project
to our experts.

Our experts build your project, delivering superior technical and functional quality within shorter timeframes.

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Solutions built by Kosmos since 2018.

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Of loyal clients ready to continue their project with Kosmos.

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Experts

100+

Projects delivered

78%

Loyal clients

4.9/5

Average rating

[ 200+ Projects ]

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us.

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A project with XGBoost?

Describe your project. Our team replies within 24 hours with free technical scoping, along with a clear estimate of costs and timelines. No commitment.

Reply within 24 hours from a project manager
or engineer.Reply within 24 hours from a project manager or engineer.
Technical scoping and quote, with no fees.
No commitment, your data stays
confidential.No commitment, your data stays confidential.