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Dimensionality reduction techniques can simplify complex data sets from "summary" of Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido
Dimensionality reduction techniques are a set of unsupervised learning methods that are used to reduce the number of features in a dataset. These techniques are particularly useful when dealing with high-dimensional data, as they can help simplify complex data sets and make them more manageable for machine learning algorithms. By reducing the number of features, dimensionality reduction techniques can help improve the performance of machine learning models by reducing the risk of overfitting and speeding up training times. One common method of dimensionality reduction is Principal Component Analysis (PCA), which works by identifying the directions in which the data varies the most and projecting the data onto a lower-dimensional subspace defined by these directions. By doing so, PCA can help capture the most important patterns in the data while discarding less relevant information. Anothe...
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