Logistic Regression - Feature Interpretation I have fit a logistic regression to predict a given binary outcome, and the estimated coefficient associated with one of the features, feature $x$ let's say, has a value $\theta$.
The interpretation of this coefficient is that an increase by one unit of feature $x$, will increase the odds of the outcome by a factor of $e^\theta$.
Is it possible to quantify/approximate by how much the "probability of the outcome" (not the odds) will increase/decrease given an increase by one unit of feature $x$ ?

Unfortuantely, the interpretation of logistic regression coefficients is difficult.
The change in probability will depend on the values of the other features, or, in other words, where on the logistic curve you start. You thus cannot tranform an odds ratio into absolute odds for logistic regression models.
A workaround to interpret a logistic regression coefficient $\theta_i$ is to set all features to their average values and compute the increase in probability for changes in its associated variable $x_i$, e.g. when it doubles.
