DriveML: An R Package for Driverless Machine Learning
- URL: http://arxiv.org/abs/2005.00478v3
- Date: Fri, 6 Aug 2021 15:02:06 GMT
- Title: DriveML: An R Package for Driverless Machine Learning
- Authors: Sayan Putatunda, Dayananda Ubrangala, Kiran Rama, Ravi Kondapalli
- Abstract summary: DriveML helps in implementing some of the pillars of an automated machine learning pipeline.
The main benefits of DriveML are in development time savings, reduce developer's errors, optimal tuning of machine learning models and errors.
- Score: 7.004573941239386
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, the concept of automated machine learning has become very
popular. Automated Machine Learning (AutoML) mainly refers to the automated
methods for model selection and hyper-parameter optimization of various
algorithms such as random forests, gradient boosting, neural networks, etc. In
this paper, we introduce a new package i.e. DriveML for automated machine
learning. DriveML helps in implementing some of the pillars of an automated
machine learning pipeline such as automated data preparation, feature
engineering, model building and model explanation by running the function
instead of writing lengthy R codes. The DriveML package is available in CRAN.
We compare the DriveML package with other relevant packages in CRAN/Github and
find that DriveML performs the best across different parameters. We also
provide an illustration by applying the DriveML package with default
configuration on a real world dataset. Overall, the main benefits of DriveML
are in development time savings, reduce developer's errors, optimal tuning of
machine learning models and reproducibility.
Related papers
- AutoMMLab: Automatically Generating Deployable Models from Language
Instructions for Computer Vision Tasks [39.71649832548044]
AutoMMLab is a general-purpose LLM-empowered AutoML system that follows user's language instructions.
The proposed AutoMMLab system effectively employs LLMs as the bridge to connect AutoML and OpenMMLab community.
Experiments show that our AutoMMLab system is versatile and covers a wide range of mainstream tasks.
arXiv Detail & Related papers (2024-02-23T14:38:19Z) - Large Language Models for Automated Data Science: Introducing CAAFE for
Context-Aware Automated Feature Engineering [52.09178018466104]
We introduce Context-Aware Automated Feature Engineering (CAAFE) to generate semantically meaningful features for datasets.
Despite being methodologically simple, CAAFE improves performance on 11 out of 14 datasets.
We highlight the significance of context-aware solutions that can extend the scope of AutoML systems to semantic AutoML.
arXiv Detail & Related papers (2023-05-05T09:58:40Z) - AutoML-GPT: Automatic Machine Learning with GPT [74.30699827690596]
We propose developing task-oriented prompts and automatically utilizing large language models (LLMs) to automate the training pipeline.
We present the AutoML-GPT, which employs GPT as the bridge to diverse AI models and dynamically trains models with optimized hyper parameters.
This approach achieves remarkable results in computer vision, natural language processing, and other challenging areas.
arXiv Detail & Related papers (2023-05-04T02:09:43Z) - OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge
Collaborative AutoML System [85.8338446357469]
We introduce OmniForce, a human-centered AutoML system that yields both human-assisted ML and ML-assisted human techniques.
We show how OmniForce can put an AutoML system into practice and build adaptive AI in open-environment scenarios.
arXiv Detail & Related papers (2023-03-01T13:35:22Z) - Towards Green Automated Machine Learning: Status Quo and Future
Directions [71.86820260846369]
AutoML is being criticised for its high resource consumption.
This paper proposes Green AutoML, a paradigm to make the whole AutoML process more environmentally friendly.
arXiv Detail & Related papers (2021-11-10T18:57:27Z) - Privileged Zero-Shot AutoML [16.386335031156]
This work improves the quality of automated machine learning (AutoML) systems by using dataset and function descriptions.
We show that zero-shot AutoML reduces running and prediction times from minutes to milliseconds, consistently across datasets.
arXiv Detail & Related papers (2021-06-25T16:31:05Z) - AutoGL: A Library for Automated Graph Learning [67.63587865669372]
We present Automated Graph Learning (AutoGL), the first dedicated library for automated machine learning on graphs.
AutoGL is open-source, easy to use, and flexible to be extended.
We also present AutoGL-light, a lightweight version of AutoGL to facilitate customizing pipelines and enriching applications.
arXiv Detail & Related papers (2021-04-11T10:49:23Z) - Resource-Aware Pareto-Optimal Automated Machine Learning Platform [1.6746303554275583]
novel platform Resource-Aware AutoML (RA-AutoML)
RA-AutoML enables flexible and generalized algorithms to build machine learning models subjected to multiple objectives.
arXiv Detail & Related papers (2020-10-30T19:37:48Z) - Adaptation Strategies for Automated Machine Learning on Evolving Data [7.843067454030999]
This study is to understand the effect of data stream challenges such as concept drift on the performance of AutoML methods.
We propose 6 concept drift adaptation strategies and evaluate their effectiveness on different AutoML approaches.
arXiv Detail & Related papers (2020-06-09T14:29:16Z) - AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data [120.2298620652828]
We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models.
Tests on a suite of 50 classification and regression tasks from Kaggle and the OpenML AutoML Benchmark reveal that AutoGluon is faster, more robust, and much more accurate.
arXiv Detail & Related papers (2020-03-13T23:10:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.