Techniques like Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Singular Value Decomposition (SVD) for reducing the number of features in the dataset.
Dimensionality Reduction Techniques Introduction Dimensionality reduction is a technique used to reduce the number of input variables in a dataset. Importance Helps in data visualization. Reduces computational complexity. Prevents overfitting. Common Techniques a. Principal Component Analysis (PCA) Linear technique to transform data. Identifies the directions of maximum variance. b. t-...…
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