Feature Selection & Dimensionality Reduction

Feature selection and dimensionality reduction address the critical challenge of focusing machine learning algorithms on truly relevant information while discarding noise—enhancing both performance and interpretability by creating more parsimonious models. Feature selection methods identify the most predictive variables from potentially hundreds or thousands of candidates, reducing model complexity without sacrificing accuracy. Filter approaches apply statistical tests to evaluate features independently of any model—using correlations, mutual information, or ANOVA F-values to rank variables by their relationship with the target. Wrapper methods evaluate feature subsets by training models and measuring performance—recursive feature elimination iteratively removes the weakest features while monitoring accuracy changes.

Embedded techniques incorporate feature selection directly into the model training process—LASSO regression shrinks irrelevant coefficients precisely to zero through L1 regularization, while tree-based models naturally quantify feature importance through their splitting criteria. Dimensionality reduction takes a transformational approach, creating new features that compress information from the original set. Principal Component Analysis (PCA) identifies orthogonal directions of maximum variance, projecting data onto these principal components to preserve information while drastically reducing dimensions. t-Distributed Stochastic Neighbor Embedding (t-SNE) excels at visualization by preserving local neighborhood relationships, making it valuable for exploring high-dimensional data in two dimensions. Autoencoders leverage neural networks to learn compact data representations in their hidden layers, automatically discovering efficient encodings that capture essential patterns while filtering noise. These techniques collectively address the 'curse of dimensionality'—the paradoxical phenomenon where having too many features relative to observations can actually degrade model performance by increasing noise and computational complexity while making it harder to identify genuine patterns.