PCA Using Python: A Tutorial | Built In
PCA Using Python: A Tutorial | Built In
Feb 23, 2024
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What is Principal Component Analysis (PCA) in ML?
What is Principal Component Analysis (PCA) in ML?
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Malkesh Dalia on Instagram: "The covariance matrix is the geometry of your data. When you plot data points, you don’t just care where they are. You care how they move together. Covariance measures exactly that: how two variables vary relative to each other. • Positive covariance means they increase together • Negative covariance means one rises while the other falls • Zero covariance means no linear relationship Stack these relationships into a matrix, and you get a full picture of how your data
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Artificial Intelligence | AI on Instagram: "Principal Component Analysis (PCA) is an unsupervised machine learning technique used for dimensionality reduction while preserving as much variance as possible in a dataset. It transforms the original correlated variables into a new set of uncorrelated variables called principal components. The process begins by centering the data (which is subtracting the mean), then computing the covariance matrix to capture the relationships between variables. Eige
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AI • Machine Learning • Tech on Instagram: "PCA begins by centering the data so each feature has zero mean, which ensures variance is measured correctly. The algorithm then computes the covariance matrix of the data to capture how features vary together. By performing an eigenvalue decomposition of this covariance matrix or an SVD of the data matrix, PCA computes eigenvectors that define the principal "directions" and eigenvalues that quantify how much variance each direction contributes. The da
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