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Example Question

You are building a machine learning model to predict customer churn. You have a dataset with 10,000 samples and 50 features. Which approach would be most appropriate for feature selection?

Use Principal Component Analysis (PCA) to reduce dimensionalityUse recursive feature elimination with cross-validationUse all features and let the model learn automaticallyUse correlation matrix to remove highly correlated features

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