Lecture "Applied data science: Classification" includes content: Classification - logistic regression review ; classification evaluation metrics; the expected value framework; . We invite you to consult! | Classification Overview 1. Introduction 8. Validation 2. Application 9. Regularisation 3. EDA 10. Clustering 4. Learning Process 11. Evaluation 5. Bias-Variance Tradeoff 12. Deployment 6. Regression review 13. Ethics 7. Classification Lecture outline - Classification - Logistic regression review - Classification evaluation metrics - The expected value framework Classification problems Response is categorical . credit card default Yes No favourite movie types Action Drama Animation Exemplary techniques - logistic regression classification tree K-NN etc. Logistic regression formulation Logistic regression coefficients are estimated by maximising the likelihood function Logistic regression example responding Yes No student_Yes 127 2817 student_No 206 6850 Total 333 9667 Training set responding Yes No student_Yes 84 1959 student_No 150 4808 Total 234 6767 Test set responding Yes No student_Yes 43 858 student_No 56 2042 Total 99 2900 Logistic regression results Logistic regression results interpretation Prediction from multiple classifiers The ROC curve The ROC curve Each point corresponds to a confusion matrix Point A is more conservative than B which is more conservative than C Points that are closer to the upper left are preferred. Point 0 1 represents the perfect classifier Points along the diagonal represent random guessing - no classifiers should be in the lower right The ROC curves from different classifiers p n Predicted Yes 46 12 Predicted No 53 2888 The expected value analytical framework The targeted marketing example. Assume that we sell the product for 200 production related cost is 100 and shipping and handling cost is 1. What would be the minimum probability of responding we should target. Expected value of a classifier Expected value of a classifier From the above example let s use as the threshold and assume the matrix of cost benefit information is as below. What would be total expected value of the logistic regression classifier per customer
