Dimensionality reduction is a step in the Data Preprocessing phase of machine learning that helps simplify models, enhance interpretability, and improve computational efficiency.
Its a technique used to reduce the number of input variables (features) in a dataset while retaining as much information as possible. This process is essential for several reasons:
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Improves Model Performance: Reducing the number of features can help improve the performance of machine learning models by minimizing overfitting and reducing noise.
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Enhances Visualization: It allows for easier visualization of high-dimensional data by projecting it into lower dimensions (e.g., 2D or 3D).
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Reduces Computational Cost: Fewer features mean less computational power and time required for training models.
Common Techniques
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Principal Component Analysis (Principal Component Analysis): A statistical method that transforms the data into a new coordinate system, where the greatest variance by any projection lies on the first coordinate (principal component/orthogonal components ), the second greatest variance on the second coordinate, and so on.
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t-Distributed Stochastic Neighbor Embedding (t-SNE): A technique particularly well-suited for visualizing high-dimensional data by reducing it to two or three dimensions while preserving the local structure of the data. t-SNE is a non-linear technique used for visualization and dimensionality reduction by preserving pairwise similarities between data points, making it suitable for exploring high-dimensional data.
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Linear Discriminant Analysis method used for both classification and dimensionality reduction, which finds a linear combination of features that best separates two or more classes.