forester: A Tree-Based AutoML Tool in R

• URL: http://arxiv.org/abs/2409.04789v1
• Date: Sat, 7 Sep 2024 10:39:10 GMT
• Title: forester: A Tree-Based AutoML Tool in R
• Authors: Hubert Ruczyński, Anna Kozak,
• Abstract summary: The forester is an open-source AutoML package implemented in R for training high-quality tree-based models. It fully supports binary and multiclass classification, regression, and partially survival analysis tasks. With just a few functions, the user is capable of detecting issues regarding the data quality, preparing the preprocessing pipeline, training and tuning tree-based models, evaluating the results, and creating the report for further analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The majority of automated machine learning (AutoML) solutions are developed in Python, however a large percentage of data scientists are associated with the R language. Unfortunately, there are limited R solutions available. Moreover high entry level means they are not accessible to everyone, due to required knowledge about machine learning (ML). To fill this gap, we present the forester package, which offers ease of use regardless of the user's proficiency in the area of machine learning. The forester is an open-source AutoML package implemented in R designed for training high-quality tree-based models on tabular data. It fully supports binary and multiclass classification, regression, and partially survival analysis tasks. With just a few functions, the user is capable of detecting issues regarding the data quality, preparing the preprocessing pipeline, training and tuning tree-based models, evaluating the results, and creating the report for further analysis.

Related papers

• Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping [0.0]
  The Deep Fast Machine Learning Utils (DFMLU) library provides tools designed to automate and enhance aspects of machine learning processes. DFMLU offers functionalities that support model development and data handling. This manuscript presents an overview of DFMLU's functionalities, providing Python examples for each tool.
  arXiv  Detail & Related papers  (2024-09-14T21:39:17Z)
• Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications [2.663744975320783]
  We find that L2-regularized logistic regression performs similar to state-of-the-art automated machine learning (AutoML) frameworks. We recommend to consider logistic regression as the first choice for data-scarce applications.
  arXiv  Detail & Related papers  (2024-05-13T11:43:38Z)
• 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)
• ALBench: A Framework for Evaluating Active Learning in Object Detection [102.81795062493536]
  This paper contributes an active learning benchmark framework named as ALBench for evaluating active learning in object detection. Developed on an automatic deep model training system, this ALBench framework is easy-to-use, compatible with different active learning algorithms, and ensures the same training and testing protocols.
  arXiv  Detail & Related papers  (2022-07-27T07:46:23Z)
• Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation [97.42894942391575]
  We propose FAST-DAD to distill arbitrarily complex ensemble predictors into individual models like boosted trees, random forests, and deep networks. Our individual distilled models are over 10x faster and more accurate than ensemble predictors produced by AutoML tools like H2O/AutoSklearn.
  arXiv  Detail & Related papers  (2020-06-25T09:57:47Z)
• DriveML: An R Package for Driverless Machine Learning [7.004573941239386]
  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.
  arXiv  Detail & Related papers  (2020-05-01T16:40:25Z)
• Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution [1.5224436211478214]
  This paper describes a novel grammar-based framework that adapts Dynamic Structured Grammatical Evolution (DSGE) to the evolution of Scikit-Learn classification pipelines. The experimental results include comparing AutoML-DSGE to another grammar-based AutoML framework, Resilient ClassificationPipeline Evolution (RECIPE)
  arXiv  Detail & Related papers  (2020-04-01T09:31:34Z)
• 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)
• PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning [55.32009000204512]
  We present PyODDS, an automated end-to-end Python system for Outlier Detection with Database Support. Specifically, we define the search space in the outlier detection pipeline, and produce a search strategy within the given search space. It also provides unified interfaces and visualizations for users with or without data science or machine learning background.
  arXiv  Detail & Related papers  (2020-03-12T03:30:30Z)
• PHOTONAI -- A Python API for Rapid Machine Learning Model Development [2.414341608751139]
  PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences.
  arXiv  Detail & Related papers  (2020-02-13T10:33:05Z)
• Multi-layer Optimizations for End-to-End Data Analytics [71.05611866288196]
  We introduce Iterative Functional Aggregate Queries (IFAQ), a framework that realizes an alternative approach. IFAQ treats the feature extraction query and the learning task as one program given in the IFAQ's domain-specific language. We show that a Scala implementation of IFAQ can outperform mlpack, Scikit, and specialization by several orders of magnitude for linear regression and regression tree models over several relational datasets.
  arXiv  Detail & Related papers  (2020-01-10T16:14:44Z)

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.