Although Linear Least Squares Regression is simple and precise, it can be inefficient when matrices get very large. Cholesky decomposition is another approach to solve matrices efficiently by Linear Least Squares, as it decomposes a matrix into a lower and upper triangular matrix (L and LT). Finally, linear regression with Cholesky decomposition...
Continue reading...January 2018
Linear Least Squares Regression with TensorFlow
Linear Least Squares Regression is by far the most widely used regression method, and it is suitable for most cases when data behavior is linear. By definition, a line is defined by the following equation: For all data points (xi, yi) we have to minimize the sum of the squared...
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