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Clojure for Data Science by Henry Garner

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Multiple regression with gradient descent

When we ran multiple linear regression in Chapter 3, Correlation, we used the normal equation and matrices to quickly arrive at the coefficients for a multiple linear regression model. The normal equation is repeated as follows:

Multiple regression with gradient descent

The normal equation uses matrix algebra to very quickly and efficiently arrive at the least squares estimates. Where all data fits in memory, this is a very convenient and concise equation. Where the data exceeds the memory available to a single machine however, the calculation becomes unwieldy. The reason for this is matrix inversion. The calculation of is not something that can ...

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