Lecture 22 – Logistic Regression, Part 1

Presented by Presented by Fernando Perez, Suraj Rampure

Content by Suraj Rampure, Josh Hug, Joseph Gonzalez, Ani Adhikari

A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.

Video Quick Check
22.0
Logistics
22.1
Classification, and a brief overview of the machine learning taxonomy.
22.1
22.2
Pitfalls of using least squares to model probabilities. Creating a graph of averages to motivate the logistic regression model.
22.2
22.3
Deriving the logistic regression model from the assumption that the log-odds of the probability of belonging to class 1 is linear.
22.3
22.4
Formalizing the logistic regression model. Exploring properties of the logistic function. Interpreting the model coefficients.
22.4
22.5
Discussing the pitfalls of using squared loss with logistic regression.
22.5
22.6
Introducing cross-entropy loss, as a better alternative to squared loss for logistic regression.
22.6
22.7
Using maximum likelihood estimation to arrive at cross-entropy loss.
22.7
22.8
Demo of using scikit-learn to fit a logistic regression model. An overview of what's coming next.
22.8