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MLJ
A Machine Learning Framework for Julia

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Model Browser

Reference Manual

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 | Correcting Class Imbalance

Models

Model Search | Loading Model Code | Transformers and Other Unsupervised Models | Simple User Defined Models | List of Supported Models | Third Party Packages

Meta-algorithms

Evaluating Model Performance | Tuning Models | Composing Models | Controlling Iterative Models | Learning Curves| Correcting Class Imbalance | Thresholding Probabilistic Predictors

Composition

Composing Models | Linear Pipelines | Target Transformations | Homogeneous Ensembles | Model Stacking | Learning Networks| Correcting Class Imbalance

Integration

Logging Workflows | OpenML Integration

Customization and Extension

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

Miscellaneous

Weights | Acceleration and Parallelism | Performance Measures