The main application of automated machine learning (AutoML) is to automate and facilitate the process of supervised machine learning for those who do not have enough knowledge of machine learning but need to do so. For this reason, it is said that if you use automated learning, you do not need to have technical knowledge in the field of machine learning.
To be more precise, automated learning is the automation of the supervised machine learning process, which makes the creation and use of supervised machine learning models much easier by implementing systematic processes.
One of the best tools for doing online automated machine learning is Bigpro1 platform, which we introduced to you earlier. To use AutoML in Bigpro1, you can first read the article on doing auto learning in Bigpro1, or go directly to the Bigpro1 dashboard and start your auto machine learning project.
In this article, we are going to introduce one of the articles that has worked well in expressing the application of automated machine learning.
In Bigpro1, user can login to Bigpro1 dashboard in data mining dashboard, after uploading the dataset and selecting the target column and the automatic machine learning algorithm option, start your processing with just a simple click and without making any settings. After processing the automated learning algorithm in Bigpro1, the system automatically compares all the machine learning algorithms and selects the best data-driven model for automated machine learning online.
As we have said, in this article we are going to introduce one of the articles that has expressed the application of automated machine learning in the field of medicine. This paper is an open source Python package that explores the possibility of predicting drug interventions by automating the construction of supervised machine learning models. And by doing this automation, he tried to eliminate the tedious and long stages of machine learning.
This article also shows that its predictive accuracy outperforms other machine learning frameworks, including H2O with default settings. An open source, all-in-one automated machine learning package for predicting clinical outcome.
Pharm-AutoML: An open-source, end-to-end automated machine learning package for clinical outcome prediction
Although interest in using machine learning (ML) to support drug development is growing, the technical barriers associated with complex algorithms have limited widespread acceptance. In response, we developed Pharm-AutoML, an open source Python package that enables users to automate the construction of ML models and predict clinical outcomes, particularly in the field of drug interventions. In particular, our approach simplifies tedious steps in the ML workflow, including data preprocessing, model tuning, model selection, analysis of results, and model interpretation. In addition, our open source package helps identify the most predictable machine learning pipeline among the defined search spaces by selecting the best data preprocessing strategy and setting the ML model meta-parameters. This package now supports multi-class classification tasks and additional functions are being developed. Using a set of five publicly available biomedical datasets, we demonstrate that our Pharm-AutoML performs better than other machine learning frameworks, including H2O with default settings, by showing improved classification prediction accuracy. We further demonstrate how model interpretation methods can be used to help explain the ML pipeline accurately to end users. Pharm-AutoML provides advanced clinical predictions and scientific insights to novice and expert users. What are the important points of studying current knowledge on the subject?
Machine learning (ML) is a powerful way to analyze complex healthcare data, and Auto ML (AutoML) offers a way to automate tedious steps in the ML pipeline. However, current AutoML frameworks do not meet the needs of biopharmaceutical or health researchers due to the lack of end-to-end automation of ML pipelines, including data preprocessing and model interpretation. What question did this study address?
Pharm-AutoML is designed to provide an integrated AutoML solution, enabling biopharmaceutical researchers to automate the construction of ML models, predict clinical outcomes, and interpret ML models. What does this study add to our knowledge?
Pharm-AutoML automates the ML pipeline from data preprocessing, model tuning, model selection, analysis of results, and model interpretation. We show that such a global workflow can perform better than the current implementation of ML and AutoML.
How might this change discovery, development, and / or drug treatment?
Pharm-AutoML helps accelerate the development, deployment, and interpretation of the ML model, thus facilitating the use of ML to predict results and gain insights into clinical trials and pharmaceutical research.