Stt Intro To Statistical Learning Ppt Download

Stt Intro To Statistical Learning Ppt Download Presentation on theme: "stt : intro. to statistical learning"— presentation transcript: 1 stt592 002: intro. to statistical learning introduction chapter 01 disclaimer: "some of the figures in this presentation are taken from "an introduction to statistical learning, with applications in r" (springer, 2013) with permission from the authors: g. james, d. witten, t. hastie and r. tibshirani ". Stt : intro. to statistical learning pros and cons of decision trees pros: trees are very easy to explain to people (probably even easier than linear regression) trees can be plotted graphically, and are easily interpreted even by non expert they work fine on both classification and regression problems cons: trees don’t have the same prediction accuracy as some of more complicated approaches.

Stt Intro To Statistical Learning Ppt Download Stt : intro. to statistical learning k nearest neighbors (knn) classifier (sec2.2) a small training data set: 6 blue and 6 orange observations. goal: to make a prediction for the black cross. consider k=3. knn identify 3 observations that are closest to the cross. this neighborhood is shown as a circle. This book provides an introduction to statistical learning methods. it is aimed for upper level undergraduate students, masters students and ph.d. students in the non mathematical sciences. the book also contains a number of r labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable. Download presentation stt 592 002: intro. to statistical learning 1 unsupervised learning chapter 10 disclaimer: this ppt is modified based on iom 530: intro. to statistical learning stt 592 002: intro. to statistical learning outline Øprinciple component analysis (pca) Øwhat is clustering?. Stt 592 002: intro. to statistical learning 1 classification methods chapter 04 (part 01).

Stt Intro To Statistical Learning Ppt Download Download presentation stt 592 002: intro. to statistical learning 1 unsupervised learning chapter 10 disclaimer: this ppt is modified based on iom 530: intro. to statistical learning stt 592 002: intro. to statistical learning outline Øprinciple component analysis (pca) Øwhat is clustering?. Stt 592 002: intro. to statistical learning 1 classification methods chapter 04 (part 01). Trevor hastie trevor hastie is a professor of statistics at stanford university. his main research contributions have been in the field of applied nonparametric regression and classification, and he has written two books in this area: "generalized additive models" (with r. tibshirani, chapman and hall, 1991), and "elements of statistical learning" (with r. tibshirani and j. friedman, springer. An introduction to statistical learning. as the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. an introduction to statistical learning provides a broad and less technical treatment of key topics in statistical learning.