Introducing fklearn: Functional Machine Learning
fklearn is a powerful library that employs functional programming principles to simplify solving real-world problems using machine learning. It draws inspiration from the popular scikit-learn library, but takes its own unique approach to model building and implementation.
Guiding Principles
fklearn operates based on several key principles that ensure effective, reliable machine learning processes:
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Real-Life Validation: The library emphasizes validations that genuinely reflect real-world scenarios. This ensures the models are trained and evaluated under conditions similar to what they will encounter in production.
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Consistency in Production: There's a strong focus on aligning the models used in production with those that have been validated. This principle reduces discrepancies and ensures seamless transitions from testing to deployment.
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Production Readiness: With fklearn, models can be transitioned to production with minimal additional steps. This streamlining of processes decreases the time and effort required to bring a model from development to application.
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Reproducibility and Analysis: The library makes it straightforward to reproduce results and conduct in-depth analyses of model performance. This transparency is vital for understanding how models arrive at their predictions and for enhancing their future performance.
Documentation and Resources
To get started with fklearn, various resources are available:
- Documentation: Offers detailed information on the library and how to use it.
- Getting Started Guide: An excellent resource for beginners to familiarize themselves with fklearn basics.
- API Documentation: Provides technical details on the various modules and functions available within the library.
- Contributing: Information on how to contribute to the development of fklearn.
Installation
fklearn can be easily installed through pip:
pip install fklearn
For those who prefer to work with the source code directly, it can be installed via Git:
git clone [email protected]:nubank/fklearn.git
cd fklearn
git checkout master
pip install -e .
Licensing
The library is distributed under the Apache License 2.0, which allows for wide use and adaptation.
In conclusion, fklearn presents an efficient and pragmatic approach to machine learning, leveraging functional programming to create robust, production-ready models. Its focus on real-world validation and reproducibility makes it an ideal tool for data scientists and machine learning practitioners looking to optimize their workflows and enhance model reliability.