2019-07-23, 14:30–15:00, Room 349
We present MLJ, Machine Learning in Julia, a new toolbox for combining and systematically tuning machine learning models.
MLJ, an open-source machine learning toolbox written in Julia, has evolved from an early proof of concept, to a functioning well-featured prototype. Features include:
1. A flexible API for complex model composition, such as stacking.
2. Repository of externally implemented model metadata, for facilitating composite model design, and for matching models to problems through a MLR-like task interface.
3. Systematic tuning and benchmarking of models having possibly nested hyperparameters.
4. Unified interface for handling probabilistic predictors and multivariate targets.
5. Agnostic data containers
6. Careful handling of categorical data types.
In addition to demonstrating some of these features, we discuss relationships with other Julia projects in the data science domain.