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Significance of Feature selection

Feature selection is the process of identifying the most relevant features that enhance the performance of predictive models. By focusing on these key descriptors, practitioners can improve model accuracy and efficiency. This technique is essential in data analysis, as it allows for a more streamlined approach by reducing complexity and minimizing noise in the dataset, ultimately leading to better predictive outcomes.

Synonyms: Variable selection, Attribute selection, Feature extraction, Dimensionality reduction, Model selection.

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The concept of Feature selection in scientific sources

Science Books

Feature selection is the process of identifying the most relevant features that enhance a predictive model's performance, facilitating improved accuracy and efficiency in analysis and decision-making based on the data.

Significance in Scientific journals, articles, etc.:

From: The Malaysian Journal of Medical Sciences

(1) Feature selection is a process of identifying the most relevant features in a dataset, and PyMVPA offers tools for this purpose.[1] (2) This is a process, and is a technique used in machine learning to choose the most relevant variables, which impacts the performance of predictive models.[2]

This section includes scientific research in the field of healthcare, often involving traditional medicine systems such as Ayurveda, Unani, Siddha and Holistic health. It includes papers from scientific journals classified as either original, clinical or experimental research, review articles or case reports.