You have a dataset with a large number of features. How would you use Scikit-learn to select the most important features for model training?
- Use feature selection techniques like Recursive Feature Elimination (RFE) with Scikit-learn's feature selection classes such as RFE or SelectKBest. These methods help identify the most relevant features based on their contribution to model performance.
- Use Scikit-learn's DecisionTreeClassifier to identify important features, which is not the standard approach for feature selection.
- Use Scikit-learn's GridSearchCV to perform hyperparameter tuning, which doesn't directly address feature selection.
- Use Scikit-learn's StandardScaler to scale the features, but this doesn't perform feature selection.
Scikit-learn offers various feature selection techniques, and one of the commonly used methods is Recursive Feature Elimination (RFE), which helps identify and select the most important features for model training.
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