Feature selection in Machine learning

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Feature selection in Machine Learning is the process of selecting the most relevant features (input variables) from your dataset that contribute the most to predicting the target variable. It helps improve model performance, reduces overfitting, and decreases training time. Learning about feature selection is an essential part of any machine learning course, as it ensures models are both efficient and effective in handling real-world data.

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