Answer: Support vector machines

## Explanation **Correct Answer: B (Support vector machines)** **Support Vector Machines (SVMs)** are particularly well-suited for high-dimensional feature spaces because: - They use kernel methods to handle non-linear relationships - They are effective in high-dimensional spaces - They are robust to overfitting through regularization - They can handle complex decision boundaries **Why other options are less suitable:** - **A (Linear models)**: Can suffer from overfitting with many features and may require dimensionality reduction - **C (Decision trees)**: Can overfit with many features and may require pruning or ensemble methods - **D (K-means)**: This is a clustering algorithm, not a classification method SVMs with appropriate kernels (like RBF kernel) can effectively handle classification problems with large numbers of features by finding optimal separating hyperplanes in high-dimensional spaces.

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