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• W4311557029 endingPage "e1010718" @default.
• W4311557029 startingPage "e1010718" @default.
• W4311557029 abstract "Applying computational statistics or machine learning methods to data is a key component of many scientific studies, in any field, but alone might not be sufficient to generate robust and reliable outcomes and results. Before applying any discovery method, preprocessing steps are necessary to prepare the data to the computational analysis. In this framework, data cleaning and feature engineering are key pillars of any scientific study involving data analysis and that should be adequately designed and performed since the first phases of the project. We call “feature” a variable describing a particular trait of a person or an observation, recorded usually as a column in a dataset. Even if pivotal, these data cleaning and feature engineering steps sometimes are done poorly or inefficiently, especially by beginners and unexperienced researchers. For this reason, we propose here our quick tips for data cleaning and feature engineering on how to carry out these important preprocessing steps correctly avoiding common mistakes and pitfalls. Although we designed these guidelines with bioinformatics and health informatics scenarios in mind, we believe they can more in general be applied to any scientific area. We therefore target these guidelines to any researcher or practitioners wanting to perform data cleaning or feature engineering. We believe our simple recommendations can help researchers and scholars perform better computational analyses that can lead, in turn, to more solid outcomes and more reliable discoveries." @default.
• W4311557029 created "2022-12-27" @default.
• W4311557029 creator A5011556172 @default.
• W4311557029 creator A5039343044 @default.
• W4311557029 creator A5045802198 @default.
• W4311557029 date "2022-12-15" @default.
• W4311557029 modified "2023-10-16" @default.
• W4311557029 title "Eleven quick tips for data cleaning and feature engineering" @default.
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