How do I evaluate feature engineering techniques?

How do I evaluate feature engineering techniques for my machine learning model?

Evaluating feature engineering techniques involves assessing their impact on model performance. You can use metrics like accuracy, precision, recall, and F1 score to compare models with different features.

What specific metrics should I focus on?

Focus on metrics relevant to your problem. For classification tasks, accuracy and F1 score are crucial. For regression, consider RMSE or R-squared.

Should I use cross-validation in my evaluation?

Yes, cross-validation is essential. It helps ensure that your evaluation is robust and not dependent on a specific train-test split.

How can I visualize the impact of feature engineering?

You can use techniques like feature importance plots, SHAP values, or partial dependence plots to visualize the impact of your features on model predictions.