Recursive feature elimination (RFE) is a feature selection technique that aims to select the most important features by recursively removing the least significant ones based on a specific model's performance. This method is particularly useful in refining datasets by identifying and retaining only those features that contribute the most to the predictive capability of a model, thereby enhancing model accuracy and efficiency. RFE is often used in supervised learning but can also be relevant in unsupervised learning contexts where dimensionality reduction is needed.
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