nubank/fklearn
fklearn: Functional Machine Learning observed · 2026-08-28
Health v2 · maintenance only
89/100
- Activity 86
- Release rhythm 87
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 7
- age_days: 2744
- days_rel: 84
- days_push: 84
- n_releases_24m: 6
Adoption not part of the score
1547 stars · 174 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
fklearn is a Python machine learning library from Nubank that applies functional programming principles to build, validate, and deploy models. It wraps model backends like LightGBM, XGBoost, and CatBoost with composable, reproducible pipelines whose validation reflects real-life scenarios.
Use cases
- train and validate machine learning models with realistic validation splits
- build reproducible ML pipelines that match production behavior
- compare LightGBM, XGBoost, and CatBoost models in one framework
- analyze model results in depth with built-in evaluation tools
- deploy validated models to production with minimal extra steps
When to avoid
- you need deep learning or PyTorch-based workflows
- you prefer the vast scikit-learn ecosystem and its integrations
- you work outside Python or outside pandas-based data pipelines
Facets
library · maturity active
machine-learning data-science testing benchmarking machine-learning data-science python functional-programming scikit-learn-alternative model-validation pandas gradient-boosting causal-inference
2 sources
- readme: https://github.com/nubank/fklearn · fetched 2026-08-28 · f2ba9620a956
- registry_pypi: https://pypi.org/pypi/fklearn/json · fetched 2026-08-29 · bf51adbd7c04
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| nubank/fklearn | main | 89 |
For agents
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem