From the course: Machine Learning with Data Reduction in Excel, R, and Power BI
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Eigenvalues
From the course: Machine Learning with Data Reduction in Excel, R, and Power BI
Eigenvalues
- [Instructor] In order to create a projection space for the PCA model, we need to use eigenvalues and eigenvectors to determine how we're transforming our data points. Let's say we have a vector in a two dimensional space. The eigenvalues change the length fit which is why it's called a scaler. We often calculate eigenvalues and eigenvectors together in a model, but let's examine how to calculate eigenvalues first. To calculate the eigenvalues, we start with a covariance matrix for the selected data. If you have two dimensions in the model we'll see a matrix with two rows and two columns. Conversely, if we have four dimensions, we'll have a matrix with four rows and four columns and so on. If we manually calculate the covariance matrix we have the variance for the given dimension along the diagonal for the matrix and we have the covariance between temperature and precipitation in the spaces that aren't along the…
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