The question asks for a feature extraction method, but the provided answer choices are primarily feature selection methods. However, based on the community discussion and the context from the case study mentioned in the comments, the scenario requires analyzing the relationship between two numeric columns (MedianValue and AvgRoomsInHouse) using non-parametric statistics. Spearman correlation is a non-parametric method that assesses monotonic relationships, making it suitable here. The community consensus, supported by upvoted comments (e.g., comment_id '695009' with 7 upvotes), emphasizes that non-parametric methods are required, and Spearman fits this criterion. Pearson's correlation (B) is parametric and assesses linear relationships, which is less suitable. Mutual information (A) measures dependency but is not specifically non-parametric for correlation analysis. Fisher Linear Discriminant Analysis (D) is for classification and dimensionality reduction, not correlation analysis, and is inappropriate for this regression-like scenario. Thus, Spearman correlation (C) is the optimal choice.