(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)
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location: Section 9.4, "Probit regression," pages 293-295
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location: Section 4.3.5, "Probit regression," pages 210-212
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