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MLJ
A Machine Learning Framework for Julia

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Basics

Getting Started | Working with Categorical Data | Common MLJ Workflows | Machines | MLJ Cheatsheet

Data

Working with Categorical Data | Preparing Data | Generating Synthetic Data | OpenML Integration

Models

Model Search | Loading Model Code | Transformers and Other Unsupervised Models | More on Probabilistic Predictors | Composing Models | Simple User Defined Models | List of Supported Models | Third Party Packages

Meta-algorithms

Evaluating Model Performance | Tuning Models | Controlling Iterative Models | Learning Curves

Composition

Composing Models | Linear Pipelines | Homogeneous Ensembles | Model Stacking |

Customization and Extension

Simple User Defined Models | Quick-Start Guide to Adding Models | Adding Models for General Use | Composing Models | Internals | Modifying Behavior

Bells and whistles

Weights | Acceleration and Parallelism | Performance Measures