probit regression

(30 minutes to learn)


Probit regression is a discriminative model for classification. In this model, the binary targets are generated by sampling latent Gaussian variables whose means are linear in the inputs, and passing them through a threshold.


This concept has the prerequisites:


  • Know what the probit regression model is
  • Be able to derive the gradient descent update rules
  • Understand the relationship with logistic regression:
    • the two have similar activation functions
    • however, probit regression is more sensitive to outliers
  • Interpret the model in terms of a latent Gaussian variable and a threshold

Core resources (read/watch one of the following)


See also