Lecture 20 – Logistic Regression Part 2, Classification
Presented by Fernando Perez
Content by Suraj Rampure, Fernando Perez, Josh Hug, Joseph Gonzalez, Ani Adhikari
- video playlist
- 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.
Using thresholds to convert from predicted probabilities to classifications.
Defining several metrics of classifier performance – accuracy, precision, and recall. Confusion matrices.
Using scikit-learn to compute accuracy, precision, recall, and confusion matrices.
Exploring how threshold impacts accuracy, precision, and recall. Precision-recall curves. ROC curves. AUC.
Exploring the decision boundaries that result from a logistic regression classifier, and their relationship to the model's parameters.
Linear separability. Why we sometimes need regularization for logistic regression.
Summary. Brief introduction to multiclass classification.