Welcome to julearn’s documentation!

julearn is a user-oriented machine-learning library.

At the Applied Machine Learning (AML) group, as part of the Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), we thought that using ML in research could be simpler.

In the same way as seaborn provides an abstraction of matplotlib’s functionality aiming for powerful data visualization with minor coding, we built julearn on top of scikit-learn.

This library provides users with the possibility of testing ML models directly from pandas dataframes, while keeping the flexibiliy of using scikit-learn’s models.

You can also check out our video tutorial.

Contents:

• Installing julearn
  • Requirements
  • Installing
• Input Data
  • Using Pandas dataframes (recommended)
  • Using Numpy arrays
• Understanding the pipeline
  • Model & Problem Type
  • Preprocessing
  • The returned pipeline
• Scoring
  • Internal Scorers
  • Custom Scorers
• Available Pipeline Steps
  • Features Preprocessing
  • Target Preprocessing
  • Models
• Hyperparameters
  • Setting Hyperparameters
  • Tuning Hyperparameters
• Basic Examples
• Advanced Examples
• Reference
  • Main API functions
  • Model functions
  • Transformer functions
  • Logging
  • Cross-Validation
  • Classes
• Contributing to julearn
  • Setting up the development environment
  • Contributing with a pull request
  • Writing Examples
• Maintaining julearn
  • Versioning
  • Releasing a new version
• What’s new
  • Current (0.2.6.dev)
  • 0.2.5

Indices and tables

• Index

• Module Index

• Search Page