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Why are regression problems called regression problems? I was just wondering why regression problems are called "regression" problems What is the story behind the name? One definition for regression: "Relapse to a less perfect or developed state "
regression - When is R squared negative? - Cross Validated With linear regression with no constraints, R2 R 2 must be positive (or zero) and equals the square of the correlation coefficient, r r A negative R2 R 2 is only possible with linear regression when either the intercept or the slope are constrained so that the "best-fit" line (given the constraint) fits worse than a horizontal line
regression - What does negative R-squared mean? - Cross Validated For the top set of points, the red ones, the regression line is the best possible regression line that also passes through the origin It just happens that that regression line is worse than using a horizontal line, and hence gives a negative R-Squared Undefined R-Squared
regression - What is the reason the log transformation is used with . . . The biggest challenge this presents from a purely practical point of view is that, when used in regression models where predictions are a key model output, transformations of the dependent variable, Y-hat, are subject to potentially significant retransformation bias