When dealing with limited data in a complex model, I prioritize feature selection by: Understanding the context: Collaborate with experts to identify relevant features. Statistical analysis: Use correlation or mutual information to assess feature relevance. Regularization: Apply LASSO to shrink irrelevant features. Feature importance: Use models like Random Forest to rank features. Validation: Test models iteratively with cross-validation. Address multicollinearity: Remove highly correlated features using VIF. Dimensionality reduction: Use PCA if needed to simplify the feature space.

Focus on domain knowledge to identify features most likely to impact the model’s outcomes. Use techniques like correlation analysis and mutual information to quantify feature relevance. Apply regularization methods or simpler models to reduce overfitting and highlight key predictors. Validate your selections iteratively using cross-validation to ensure robustness.

Leverage domain knowledge to identify features with the most predictive potential. Use statistical methods like correlation analysis and feature importance scores to evaluate relevance. Apply dimensionality reduction techniques or regularization to simplify the model and prevent overfitting. Validate selections through cross-validation to ensure robustness and reliability.

When faced with limited data in complex models, prioritize feature selection by leveraging domain knowledge to identify the most relevant features. Utilize techniques like correlation analysis, statistical tests, and regularization methods (e.g., LASSO) to reduce overfitting. Additionally, methods like recursive feature elimination (RFE) and tree-based feature importance can refine selection further.