I’ve been building a Bayesian Logistic regression at work. However it’s hit a limitation, and one of the limitations is that there isn’t a linear structure between the covariates and the target variable.
In a Machine Learning paradigm I’d either use a Neural Network or I’d apply some transformation to the feature space to make the model linear.
Is anyone aware of any literature on this - does anyone have any examples?