Feature Selection Process

What is Feature Selection?

Feature Selection is the process of identifying and selecting the most relevant variables (features) from your dataset to use in building a predictive model.

Why It's Important

• Improved Accuracy: Removes irrelevant or redundant features
• Faster Training: Fewer features mean faster model training
• Better Interpretability: Easier to understand which variables matter
• Reduced Overfitting: Less chance of modeling noise

Common Methods

• Filter Methods: Rank features by statistical measures
• Wrapper Methods: Use model performance to select features
• Embedded Methods: Features selected during model training
• Recursive Feature Elimination: Iteratively removes least important features

In CMMI-DCC ML Pipelines

Feature selection helps identify:
- Most important metabolites for disease classification
- Key clinical markers for patient outcomes
- Relevant microbiome features

Related Terms

• Random Forest: Provides feature importance scores
• XGBoost: Another method for feature importance