Lecture 23 – Logistic Regression Part II, Classification
Presented by Fernando Perez
Content by Suraj Rampure, Fernando Perez, 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 | |
|---|---|---|
| 23.1 Using thresholds to convert from predicted probabilities to classifications. |
23.1 | |
| 23.2 Defining several metrics of classifier performance – accuracy, precision, and recall. Confusion matrices. |
23.2 | |
| 23.3 Using scikit-learn to compute accuracy, precision, recall, and confusion matrices. |
23.3 | |
| 23.4 Exploring how threshold impacts accuracy, precision, and recall. Precision-recall curves. ROC curves. AUC. |
23.4 | |
| 23.5 Exploring the decision boundaries that result from a logistic regression classifier, and their relationship to the model's parameters. |
23.5 | |
| 23.6 Linear separability. Why we sometimes need regularization for logistic regression. |
23.6 | |
| 23.7 Summary. Brief introduction to multiclass classification. |