When to use logistic regression or Poisson regression?
Logistic regression, also known as logit regression, is what you use when your outcome variable (dependent variable) is dichotomous. These would refer to all your research yes/no questions: Did you vote, yes or no? Do you have a disease, yes or no?
How are probabilities of a logistic regression model calibrated?
In general, scores returned by machine learning models are not necessarily well-calibrated probabilities (see my post on ROC space and AUC). The probability estimates from a logistic regression model (without regularization) are partially calibrated, though.
Are there any generalized linear models that are calibrated?
The probability estimates from a logistic regression model (without regularization) are partially calibrated, though. In fact, many generalized linear models, including linear regression, logistic regression, binomial regression, and Poisson regression, give calibrated predicted values.
How is the beta size of a Logistic Regression calculated?
Rather than estimate beta sizes, the logistic regression estimates the probability of getting one of your two outcomes (i.e., the probability of voting vs. not voting) given a predictor/independent variable (s). For our purposes, “hit” refers to your favored outcome and “miss” refers to your unfavored outcome.
What is the typical use case of offset in logistic regression?
Offset is commonly used in Poisson regression to take into account different exposure (different time periods for instance): offset = log of exposure Question: what’s the typical use case of offset in logistic regression?
What is the Poisson distribution of a GLM model?
Poisson regression is a type of a GLM model where the random component is specified by the Poisson distribution of the response variable which is a count. Before we look at the Poisson regression model, let’s quickly review the Poisson distribution.
How is a logistic regression different from a linear regression?
Some notes on the stats we generated above: Unlike linear regression, we’re using glm and our family is binomial. In logistic regression, we are no longer speaking in terms of beta sizes. The logistic function is S-shaped and constricts the range to 0-1. Thus, we are instead calculating the odds of getting a 0 vs. 1 outcome.