Answer: Support vector machines

## Explanation When dealing with large numbers of features, **Support Vector Machines (SVMs)** are particularly well-suited for classification problems because: - **Feature Space Handling**: SVMs can effectively handle high-dimensional feature spaces through the use of kernel functions - **Curse of Dimensionality**: Unlike some other methods, SVMs perform well even when the number of features exceeds the number of observations - **Maximum Margin Classification**: SVMs find the optimal separating hyperplane that maximizes the margin between classes - **Kernel Trick**: SVMs can implicitly map data to higher-dimensional spaces without explicitly computing the coordinates in that space **Why other options are less suitable**: - **Linear models (A)**: Can suffer from overfitting with many features and may require regularization - **Decision trees (C)**: Can become overly complex and prone to overfitting with many features - **K-means (D)**: This is an unsupervised clustering algorithm, not a classification method SVMs are particularly effective for text classification, image recognition, and other domains with high-dimensional feature spaces.

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