What are common techniques for feature selection in machine learning?

Feature selection is crucial for improving model performance and interpretability. Common techniques include: 1. **Filter Methods**: These use statistical measures (e.g., chi-squared, correlation coefficients) to evaluate the importance of features independently of the model, selecting those that meet a certain threshold. 2. **Wrapper Methods**: These use a predictive model to score feature subsets based on their performance, employing strategies like recursive feature elimination (RFE) to find the optimal set of features. 3. **Embedded Methods**: These methods perform feature selection as part of the model training process. Examples include the Lasso regression, which penalizes less significant features, and decision tree-based methods like Random Forests that provide feature importance scores.