Jun 18, 2019 · Typically, when you use logistic regression in a business setting, both inferential information about the variables used along with a good prediction are what stakeholders are …

Mar 26, 2016 · It is the exact opposite actually - statsmodels does not include the intercept by default. See the SO threads Coefficients for Logistic Regression scikit-learn vs statsmodels …

In my book Regression Modeling Strategies I downplay the use of residuals in logistic regression because (1) logistic regression makes no distributional assumptions and (2) there are more …

Dec 27, 2019 · The term confounder (or confounding) is used quite loosely these days. Usually, what people mean is to include additional variables into the regression model. In the medical …

Mar 3, 2014 · In linear regression Y Y is a continuous dependent variable, but in logistic regression it is regressing for the probability of a categorical outcome (for example 0 and 1).

Oct 17, 2014 · In this case, either logit or LogisticRegression could be used to fit the logistic regression model with the two indicator variables. The choice between the two methods may …

Think of the simplest example of a binary logistic model -- a model containing only an intercept. This is equivalent to the Bernoulli one-sample problem, often called (in this simple case) the …

I have a question regarding the validity of using RMSE (Root Mean Squared Error) to compare different logistic models. The response is either 0 or 1 and the predictions are probabilities …

Jun 30, 2023 · In this forum, there are opposite opinions(1), (2) on the uses of logistic regression. Ones say, it is a classification model and others say it is a prediction model. Therefore, the …

33 I am using Python's scikit-learn to train and test a logistic regression. scikit-learn returns the regression's coefficients of the independent variables, but it does not provide the coefficients' …