Μπορούν να βρεθούν οι παράμετροι της γραμμικής παλινδρόμησης αναλυτικά, αντί για με την μέθοδο της κλίσης;

Yes, the parameters of linear regression can be found analytically using the least squares method. For the model ( y = X\beta + \varepsilon ), where (X) is the feature matrix, (y) is the target value vector, the parameters (\beta) are found by solving the equation:

[ \beta = (X^T X)^{-1} X^T y ]

This method provides an exact solution when the matrix (X^T X) is invertible. Gradient descent is used when the data size is very large or the matrix is non-invertible, or for more complex models.