Lecture 19 – Logistic Regression Part 2, Classification
by Suraj Rampure (Summer 2020)
Make sure to complete the Quick Check questions in between each video. These are ungraded, but it’s in your best interest to do them.
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.