[ Tech stack ]
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.
[ Why XGBoost at Dexon ]
What this technology does well, and why we use it.
Typical usage: 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.
[ Complementary technologies ]
The building blocks we often mobilise alongside.
A stack rarely exists alone. Here are the technologies Dexon most often pairs with this one, through pipeline habits, usage similarity or internal mastery. Click on a building block to see its scope.
[ Reassurance ]
- 0+
- custom projects delivered
- 30
- engineers, designers, project managers
- 80 %
- from top French schools
- 24 h
- average reply time
[ They trust us ]
More than 100 French and European companies trust us
[ Press ]
They talk about us.
Nationwide coverage by BFM Business, Le Figaro, Challenges, La Tribune and CNews. An outside reading of our work and our innovations.
