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- Principal component analysis - Wikipedia
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified
- Principal Component Analysis (PCA) - GeeksforGeeks
PCA (Principal Component Analysis) is a dimensionality reduction technique used in data analysis and machine learning It helps you to reduce the number of features in a dataset while keeping the most important information
- PCA — scikit-learn 1. 7. 1 documentation
Principal component analysis (PCA) Linear dimensionality reduction using Singular Value Decomposition of the data to project it to a lower dimensional space The input data is centered but not scaled for each feature before applying the SVD
- Principal Component Analysis (PCA): Explained Step-by-Step . . .
Principal component analysis (PCA) is a technique that reduces the number of variables in a data set while preserving key patterns and trends It simplifies complex data, making analysis and machine learning models more efficient and easier to interpret
- What is principal component analysis (PCA)? - IBM
Principal component analysis, or PCA, reduces the number of dimensions in large datasets to principal components that retain most of the original information It does this by transforming potentially correlated variables into a smaller set of variables, called principal components
- Principal Component Analysis Guide Example - Statistics by Jim
Principal Component Analysis (PCA) takes a large data set with many variables per observation and reduces them to a smaller set of summary indices These indices retain most of the information in the original set of variables Analysts refer to these new values as principal components
- Principal Component Analysis Made Easy: A Step-by-Step . . .
In this article, I show the intuition of the inner workings of the PCA algorithm, covering key concepts such as Dimensionality Reduction, eigenvectors, and eigenvalues, then we’ll implement a Python class to encapsulate these concepts and perform PCA analysis on a dataset
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