Lecture 19 – Logistic Regression Part 2, Classification
Presented by Fernando Perez
Content by Suraj Rampure, Fernando Perez, Josh Hug, Joseph Gonzalez, Ani Adhikari
- slides
- video playlist
- code
- code HTML
- (supplementary) video on cross-entropy loss and KL divergence
The Quick Check for this lecture is due Monday, November 16th at 11:59PM. A random one of the following Google Forms will give you an alphanumeric code once you submit; you should take this code and enter it into the “Lecture 19” question in the “Quick Check Codes” assignment on Gradescope to get credit for submitting this Quick Check.
| Video | Quick Check | |
|---|---|---|
| 19.1 Using thresholds to convert from predicted probabilities to classifications. |
19.1 | |
| 19.2 Defining several metrics of classifier performance – accuracy, precision, and recall. Confusion matrices. |
19.2 | |
| 19.3 Using scikit-learn to compute accuracy, precision, recall, and confusion matrices. |
19.3 | |
| 19.4 Exploring how threshold impacts accuracy, precision, and recall. Precision-recall curves. ROC curves. AUC. |
19.4 | |
| 19.5 Exploring the decision boundaries that result from a logistic regression classifier, and their relationship to the model's parameters. |
19.5 | |
| 19.6 Linear separability. Why we sometimes need regularization for logistic regression. |
19.6 | |
| 19.7 Summary. Brief introduction to multiclass classification. |
N/A |