RFE works by iteratively building a model and ranking features based on their importance. Here's a general workflow: 1. Initial Model Training: Train the model on the entire set of features. 2. Feature Ranking: Rank features based on their importance. 3. Feature Elimination: Remove the least important feature(s). 4. Model Re-training: Re-train the model on the reduced set of features. 5. Repeat: Repeat steps 2-4 until the desired number of features is reached.
For example, in a study on breast cancer, RFE can be used to identify the most significant genes from a dataset of thousands of gene expressions, thereby focusing on the most relevant biomarkers.
