PPT-Logistic Regression & Elastic Net

Weifeng Li and Hsinchun Chen Credits Hui Zou University of Minnesota Trevor Hastie Stanford University Robert Tibshirani Stanford University 1 Outline Logistic Regression Why Logistic Regression. SIT095. The Collection and Analysis of Quantitative Data II. Week 9. Luke Sloan. Introduction. Recap – Last Week. Workshop Feedback. Multinomial Logistic Regression in SPSS. Model Interpretation. In Class Exercise. Februari, 1 2010. Gerrit. Rooks. Sociology of Innovation. Innovation Sciences & Industrial Engineering . Phone: 5509 . email: g.rooks@tue.nl. This. . Lecture. Why. . logistic. . regression. Machine Learning 726. Classification: Linear Models. Parent. Node/. Child Node. Discrete. Continuous. Discrete. Maximum Likelihood. Decision Trees. logit. distribution. (logistic. regression. ). Classifiers:. David Kauchak. CS451 – Fall 2013. Admin. Assignment 7. logistic regression: three views. linear classifier. conditional model. logistic. linear model minimizing logistic loss. Logistic regression. Why is it called logistic regression?. PERFECTION!. This is bad. Model Convergence Status. Quasi-complete separation of data points detected.. Warning:. The maximum likelihood estimate may not exist..  . Warning:. The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable.. Comments on problem set. Sigmoidal growth curve. “Logistic Model” equation. Population dynamics. Management applications. Logistic growth. :. growth with limits. Because growth . is . typically . un 10/1. . If you’d like to work with 605 students then indicate this on your proposal.. 605 students: the week after 10/1 I will post the proposals on the wiki and you will have time to contact 805 students and join teams.. Lecture 4. September 12, 2016. School of Computer Science. Readings:. Murphy Ch. . 8.1-3, . 8.6. Elken (2014) Notes. 10-601 Introduction to Machine Learning. Slides:. Courtesy William Cohen. Reminders. SPAM. ?. The . Spambase. Data Set. Source and Origin. Goal. Instances and Attributes. Examples. Tool. Goal: classify spam from ham based on the frequencies of words in the email.. Logistic Regression. We have been known for delivering reliable cargo handling services. Our company has been credited for providing Air freight services, Freight logistic, Hassle free services for clearance of both export and import consignment at custom channel through inland container Depot located at . Machine Learning. Classification. Email: Spam / Not Spam?. Online Transactions: Fraudulent (Yes / No)?. Tumor: Malignant / Benign ?. 0: “Negative Class” (e.g., benign tumor). . 1: “Positive Class” (e.g., malignant tumor). In WLS, you . are simply treating each observation as more or less informative about the underlying relationship between X and Y. Those points that are more informative are given more 'weight', and those that are less informative are given less weight. Logistic Regression. Important analytic tool in natural and social sciences. Baseline supervised machine learning tool for classification. Is also the foundation of neural networks. Generative and Discriminative Classifiers. Logistic Regression, SVMs. CISC 5800. Professor Daniel Leeds. Maximum A Posteriori: a quick review. Likelihood:. Prior: . Posterior Likelihood x prior = . MAP estimate:. . .  . Choose . and . to give the prior belief of Heads bias .