mlaut 0.1.6

Automation of large-scale training, evaluation and benchmarking of machine learning algorithms.

MLAUT is a modelling and workflow toolbox in python, written with the aim of simplifying large scale benchmarking of machine learning strategies, e.g., validation, evaluation and comparison with respect to predictive/task-specific performance or runtime. Key features are:

• automation of the most common workflows for benchmarking modelling strategies on multiple datasets including statistical post-hoc analyses, with user-friendly default settings.

• unified interface with support for scikit-learn strategies, keras deep neural network architectures, including easy user extensibility to (partially or completely) custom strategies.

• higher-level meta-data interface for strategies, allowing easy specification of scikit-learn pipelines and keras deep network architectures, with user-friendly (sensible) default configurations

• easy setting up and loading of data set collections for local use (e.g., data frames from local memory, UCI repository, openML, Delgado study, PMLB).

• back-end agnostic, automated local file system management of datasets, fitted models, predictions, and results, with the ability to easily resume crashed benchmark experiments with long running times.