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May 5, 2012 · Regression and classification are both related to prediction, where regression predicts a value from a continuous set, whereas classification predicts the 'belonging' to the class. For example, the price of a house depending on the 'size' (in some unit) and say 'location' of the house, can be some 'numerical value' (which can be continuous): this relates to regression.
Dec 19, 2014 · The relationship between Cross-entropy, logistic loss and K-L divergence is quite natural and immersed in the definition itself.
(I assume for the purposes of this answer that the data has been preprocessed to have zero mean.) Simply put, the PCA viewpoint requires that one compute the eigenvalues and eigenvectors of the covariance matrix, which is the product $\frac{1}{n-1}\mathbf X\mathbf X^\top$, where $\mathbf X$ is the data matrix.
Aug 29, 2017 · The objective of linear regression is to minimize the sum of the square of residuals $\sum_{i=1}^n{\hat\epsilon^2}$ so that we can find a estimated line that is close to the true model. However, intuitively, in order to find a estimated line that is as close as possible to the true line, we just need to minimize the distance between the true line and the estimated line.
Oct 25, 2014 · What is the difference between 'estimate of residual standard error' and 'residual standard error'? Can someone please provide the formulas? Thanks!
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Yes, curve fitting and "machine learning" regression both involving approximating data with functions. Various algorithms of "machine learning" could be applied to curve fitting, but in most cases these do not have the efficiency and accuracy of more general curve fitting algorithms, finding a choice of parameters for a mathematical model which gives "best fit" (variously defined) to a data set.
Feb 24, 2018 · For me, auto-correlation to auto-regression is the same as correlation to multiple regression. Namely, (auto)-correlation is the "state of the nature" that is modeled with auto-regression. However, can be modeled with moving-average as well, or both - e.g., AR(I)MA models.
Oct 23, 2020 · Suppose we have some data points $\mathcal{D}=\{(x_i,y_i)\}_{0\le i\le N}$ and we would like to do some linear regression analysis on these points. Let's stick to two methods, least squares (i.e. minimising the L2 norm of the residues) and L1 regression (i.e. using L1 norm). I have more often encountered the first rather than the latter.
Oct 2, 2020 · The first formula you showed is the constrained optimization formula of lasso, while the second formula is the equivalent regression or Lagrangean representation. Notice that the off-hand constraint in the first has been absorbed into the objective function in the second.
