[ Tech stack ]
scikit-learn
The reference Python toolkit for classical machine learning.
scikit-learn covers classification, regression, clustering, dimensionality reduction, model selection and preprocessing, with a consistent fit/predict API across every algorithm. Built on NumPy and SciPy, it's the right choice for tabular data when deep learning is overkill and you want reliable models that are fast to train and simple to ship to production.
[ Why scikit-learn at Dexon ]
What this technology does well, and why we use it.
Typical usage: Scoring, customer segmentation and prediction on tabular data.
- 01
Uniform fit/predict/transform API across dozens of algorithms.
- 02
Pipelines and cross-validation for a reproducible ML workflow.
- 03
Model selection: grid/random search, metrics, calibration.
- 04
Open source (BSD), outstanding docs, NumPy/SciPy ecosystem.
[ 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
Discover what the business and tech press say about us. An outside reading of our work and our innovations.
