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DynaML is a Scala environment for conducting research and education in Machine Learning. DynaML comes packaged with a powerful library of classes for various predictive models and a Scala REPL where one can not only build custom models but also play around with data work-flows.
DynaML uses the newly minted Wisp plotting library to generate aesthetic charts of common model validation metrics.
DynaML was born out of the need to have a performant, extensible and easy to use Machine Learning research environment. Scala was a natural choice for these requirements due to its sprawling data science ecosystem (i.e. Apache Spark), its functional object-oriented duality and its interoperability with the Java Virtual Machine.
DynaML is a fledgling open source project that is in a phase of rapid expansion. Currently it supports.
- Binary Classification/Regression with LS-SVM
- Regression with Gaussian Processes
- Model tuning ..* Grid Search ..* Maximum Likelihood (ML-II) ..* Coupled Simulated Annealing
- Model validation metrics (RMSE, Area under ROC)
- Entropy based data subset selection
- Data Pipes for configurable workflows
Going ahead we aim to introduce (but not limit to)
- Neural Network based models
- Time Series Models
- Sampling based Bayesian models
- GPU support