What is the importance of feature selection in machine learning?

What is the importance of feature selection in machine learning?

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

Can you explain how feature selection impacts model performance?

Certainly! By selecting the most relevant features, models can focus on the data that truly matters, leading to better predictions.

What methods are commonly used for feature selection?

Common methods 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.