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.
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Release files for mlaut 0.2.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| mlaut-0.2.0.tar.gz | 29.0 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mlaut-0.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 68.4 kB
Release files / mlaut-0.2.0.tar.gz
| Download URL | mlaut-0.2.0.tar.gz |
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| Size | 29.0 kB |
| Tags | Source |
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Release files / mlaut-0.2.0-py3-none-any.whl
| Download URL | mlaut-0.2.0-py3-none-any.whl |
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| Size | 39.4 kB |
| Tags | Python 3 |
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