Dimensionality reduction simplifies data by removing irrelevant features from "summary" of Machine Learning by Ethem Alpaydin
Dimensionality reduction is a process that simplifies data by removing irrelevant features. This concept is essential in machine learning because it helps improve the performance of algorithms by reducing the complexity of the data. When dealing with high-dimensional data, it can be challenging to extract meaningful patterns and insights. By reducing the number of features, we can focus on the most important ones and discard the rest. Irrelevant features can introduce noise into the data, making it harder for machine learning algorithms to identify patterns and make accurate predictions. Dimensionality reduction techniques help eliminate this noise by selecting only the most relevant features that contribute to the overall structure of the data. By doing so, we can improve the efficiency and effectiveness of machine learning models. One common ...
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