Dimensionality Reduction
Dimensionality reduction is the process of reducing the number of features or variables in a dataset while retaining as much information as possible. This is done by transforming the data from a high-dimensional space to a lower-dimensional space, where each dimension represents a combination of the original features. The goal of dimensionality reduction is to simplify the data and make it easier to analyze, visualize, and model. It can also help to reduce noise and improve the performance of machine learning algorithms by reducing the risk of overfitting. There are two main types of dimensionality reduction techniques: feature selection, which selects a subset of the original features, and feature extraction, which creates new features that are combinations of the original features. Some common dimensionality reduction techniques include Principal Component Analysis (PCA), t-SNE, and Autoencoders.
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