How is the GLM framework used in machine learning?
Under the GLM framework, this concept generalizes to any distribution (from the exponential family) and arbitrary link functions. If y is a count of something, such as the number of coffees someone drinks on a certain day, we could model it with a GLM with a Poisson distribution and the natural logarithm as the link function:
How are Loglinear Models and glmS related?
They are related in a sense that the loglinear models are more general than logit models, and some logit models are equivalent to certain loglinear models (e.g. consider the admissions data example or boys scout example).
How are GLMs used to predict link functions?
GLMs predict link functions instead of the raw observations. Think of them as a way of linking your observations to your model. The best way to explain a link function is to contrast it against a transformation function.
How is a GLM used in a logistic regression model?
If y is a count of something, such as the number of coffees someone drinks on a certain day, we could model it with a GLM with a Poisson distribution and the natural logarithm as the link function: The logistic regression model is also a GLM that assumes a Bernoulli distribution and uses the logit function as the link function.
Why does are GLM not converge with default setting?
I am surprised that R’s glm will “break” (not converge with default setting) for the following “toy” example (binary classification with ~50k data, ~10 features), but glmnet returns results in seconds. Am I using glm incorrectly (for example, should I set max iteration, etc.), or is R’s glm not good for big data setting?
How is the function summary used in GLM?
The function summary (i.e., summary.glm) can be used to obtain or print a summary of the results and the function anova (i.e., anova.glm) to produce an analysis of variance table. The generic accessor functions coefficients, effects, fitted.values and residuals can be used to extract various useful features of the value returned by glm.