What are the most common feature selection techniques in ML?

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Feature selection is a crucial step in machine learning, as it can improve the performance, interpretability, and efficiency of your models. But how do you choose the best features for your data and problem? In this article, we will explore some of the most common feature selection techniques in ML, and how they differ in terms of their goals, assumptions, and methods.

Key takeaways from this article
  • Correlation-driven selection:
    Filter methods quickly identify features with high correlation to the target variable while minimizing inter-correlation. This approach is efficient and independent of specific algorithms, making it ideal for initial feature reduction.### *Optimize with recursive elimination:Wrapper methods like recursive feature elimination tailor feature selection to improve model performance. Though computationally intensive, this technique ensures that interactions and dependencies among features are considered, enhancing accuracy.
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