Is predictive error the same as goodness of fit?
That is, goodness of fit is how well a model can “predict” data points you’ve already used to estimate its parameters, whereas predictive accuracy is how well a model can predict new data points, for which it hasn’t yet seen the true value of the dependent variable.
What does goodness of fit indicate?
The goodness-of-fit test is a statistical hypothesis test to see how well sample data fit a distribution from a population with a normal distribution. Goodness-of-fit establishes the discrepancy between the observed values and those that would be expected of the model in a normal distribution case.
What’s the difference between goodness of fit and predictive performance?
Within prediction modeling the goodness of fit of a model is referred to as predictive performance. While goodness of fit and predictive performance use some of the same tests the main difference is that goodness of fit tests are done on the original data while predictive performance uses new data.
How is the goodness of fit test used?
The goodness of fit of a statistical model is commonly assessed by describing how well the model fits the observed data. Many tests for goodness of fit evaluate the differences between the observed values, the actual data, and the expected values from the model.
How is the predictive performance of a model assessed?
The predictive performance of a model can be assessed by discrimination and calibration measures (Steyerberg, et al., 2010). In the setting of logistic regression, a discrimination statistic is used to determine how well the model can distinguish between patients having the outcome or not.
Which is the best measure of predictive discrimination?
Note that even though a receiver operating characteristic curve is seldom appropriate, its area (also called c-index or concordance probability from the Wilcoxon-Mann-Whitney test) is a good summary measure of pure predictive discrimination.