Lecture 20 – 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
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 | |
|---|---|---|
| 20.1 Using thresholds to convert from predicted probabilities to classifications. |
20.1 | |
| 20.2 Defining several metrics of classifier performance – accuracy, precision, and recall. Confusion matrices. |
20.2 | |
| 20.3 Using scikit-learn to compute accuracy, precision, recall, and confusion matrices. |
20.3 | |
| 20.4 Exploring how threshold impacts accuracy, precision, and recall. Precision-recall curves. ROC curves. AUC. |
20.4 | |
| 20.5 Exploring the decision boundaries that result from a logistic regression classifier, and their relationship to the model's parameters. |
20.5 | |
| 20.6 Linear separability. Why we sometimes need regularization for logistic regression. |
20.6 | |
| 20.7 Summary. Brief introduction to multiclass classification. |
N/A |