Automated Machine Learning Overview

Budjač, Roman; Nikmon, Marcel; Schreiber, Peter; Zahradníková, Barbora; Janáčová, Dagmar

doi:10.2478/rput-2019-0033

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…AutoGluon provides a user-friendly interface and tools that allow for data to be trained effectively within a single line of code as it automatically balances efficiency and performance, allowing for less headaches with hyperparameter editing [28]. AutoKeras is an AutoML system based on Keras and TensorFlow and was developed at DATA Lab at the Texas AM campus [29][30][31]. AutoKeras allows for the building and training of deep neural networks and automates the process of hyperparameter tuning and model selection with an easy-to-use interface [29].…”

Section: Automl Toolsmentioning

confidence: 99%

“…AutoKeras is an AutoML system based on Keras and TensorFlow and was developed at DATA Lab at the Texas AM campus [29][30][31]. AutoKeras allows for the building and training of deep neural networks and automates the process of hyperparameter tuning and model selection with an easy-to-use interface [29]. Auto-Sklearn was built around scikit-learn and automatically searches for the best machine learning algorithm for a dataset, along with hyperparameter optimization [32,33].…”

Section: Automl Toolsmentioning

confidence: 99%

“…AutoKeras trains deep neural networks and performs model selection and hyperparameter tuning, with little user input required [29]. Some of the highest accuracies achieved by previous research groups predicting heart disease using traditional methods were obtained using neural networks [12,13].…”

Section: Details Of the Selected Automl Toolsmentioning

confidence: 99%

“…AutoKeras offers classification and regression tools for image, text, and structured data. It also offers a TimeSeriesForecaster and more advanced tools for multi-modal and multitask analyses and customized model development [29]. Default settings were used for the AutoKeras StructuredDataClassifier, including max-trials = 100, epochs = 1000, and validation-split = 0.2.…”

Section: Details Of the Selected Automl Toolsmentioning

confidence: 99%

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Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction

Paladino,

Hughes,

Perera

et al. 2023

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Globally, over 17 million people annually die from cardiovascular diseases, with heart disease being the leading cause of mortality in the United States. The ever-increasing volume of data related to heart disease opens up possibilities for employing machine learning (ML) techniques in diagnosing and predicting heart conditions. While applying ML demands a certain level of computer science expertise—often a barrier for healthcare professionals—automated machine learning (AutoML) tools significantly lower this barrier. They enable users to construct the most effective ML models without in-depth technical knowledge. Despite their potential, there has been a lack of research comparing the performance of different AutoML tools on heart disease data. Addressing this gap, our study evaluates three AutoML tools—PyCaret, AutoGluon, and AutoKeras—against three datasets (Cleveland, Hungarian, and a combined dataset). To evaluate the efficacy of AutoML against conventional machine learning methodologies, we crafted ten machine learning models using the standard practices of exploratory data analysis (EDA), data cleansing, feature engineering, and others, utilizing the sklearn library. Our toolkit included an array of models—logistic regression, support vector machines, decision trees, random forest, and various ensemble models. Employing 5-fold cross-validation, these traditionally developed models demonstrated accuracy rates spanning from 55% to 60%. This performance is markedly inferior to that of AutoML tools, indicating the latter’s superior capability in generating predictive models. Among AutoML tools, AutoGluon emerged as the superior tool, consistently achieving accuracy rates between 78% and 86% across the datasets. PyCaret’s performance varied, with accuracy rates from 65% to 83%, indicating a dependency on the nature of the dataset. AutoKeras showed the most fluctuation in performance, with accuracies ranging from 54% to 83%. Our findings suggest that AutoML tools can simplify the generation of robust ML models that potentially surpass those crafted through traditional ML methodologies. However, we must also consider the limitations of AutoML tools and explore strategies to overcome them. The successful deployment of high-performance ML models designed via AutoML could revolutionize the treatment and prevention of heart disease globally, significantly impacting patient care.

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Section: Automl Toolsmentioning

confidence: 99%

Section: Automl Toolsmentioning

confidence: 99%

Section: Details Of the Selected Automl Toolsmentioning

confidence: 99%

Section: Details Of the Selected Automl Toolsmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating the Performance of Automated Machine Learning (AutoML) Tools for Heart Disease Diagnosis and Prediction

Paladino,

Hughes,

Perera

et al. 2023

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“…A new related trend is gaining momentum called "Automated ML" [10]. Essentially, this concept offers ML expert tools to apply ML to ML itself in order to automate repetitive tasks related to ML like the choice of the convenient dataset, data preparation or enven feature selection [11].…”

Section: Difference Between Automation ML and Automated Ml Conceptsmentioning

confidence: 99%

Automated machine learning: the new data science challenge

Slimani¹,

Slimani

Achchab

et al. 2022

IJECE

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The world is changing quite rapidly while increasingly tuning into digitalization. However, it is important to note that data science is what most technology is evolving around and data is definitely the future of everything. For industries, adopting a “data science approach” is no longer an option, it becomes an obligation in order to enhance their business rather than survive. This paper offers a roadmap for anyone interested in this research field or getting started with “machine learning” learning while enabling the reader to easily comprehend the key concepts behind. Indeed, it examines the benefits of automated machine learning systems, starting with defining machine learning vocabulary and basic concepts. Then, explaining how to, concretely, build up a machine learning model by highlighting the challenges related to data and algorithms. Finally, exposing a summary of two studies applying machine learning in two different fields, namely transportation for road traffic forecasting and supply chain management for demand prediction where the predictive performance of various models iscompared based on different metrics.

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