Skill

Feature Selection

Data Science, Analytics and AI/ML

Feature selection is a machine learning technique for choosing the most relevant subset of input variables from a dataset to use in model training, discarding redundant or irrelevant features. Methods include filter approaches (statistical tests), wrapper approaches (like recursive feature elimination), and embedded methods (like LASSO regularization). It helps reduce overfitting, improve model interpretability, and decrease computational cost, and is used by data scientists across domains like finance, healthcare, and marketing analytics.

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• Machine Learning
• Feature Engineering
• Dimensionality Reduction
• scikit-learn