Why is feature selection important in machine learning?

Why is feature selection important in machine learning?

Feature selection is crucial because it helps improve model accuracy, reduces overfitting, and decreases training time.

Can you explain how it affects model performance?

Certainly! By selecting the most relevant features, the model can focus on the most informative data, leading to better predictions.

What techniques are commonly used for feature selection?

Common techniques include filter methods, wrapper methods, and embedded methods, each with its own advantages.

How do I know which features to select?

You can use techniques like correlation analysis, recursive feature elimination, or model-based feature importance to guide your selection.