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Unformatted text preview: ibshirani 12 ESL Chapter 1 — Introduction Trevor Hastie and Rob Tibshirani 13 ESL Chapter 1 — Introduction Trevor Hastie and Rob Tibshirani Spectral Band 1 Spectral Band 2 Spectral Band 3 Spectral Band 4 Land Usage Predicted Land Usage 14 ESL Chapter 1 — Introduction Trevor Hastie and Rob Tibshirani The Supervised Learning Problem Starting point: • Outcome measurement Y (also called dependent variable, response, target) • Vector of p predictor measurements X (also called inputs, regressors, covariates, features, independent variables) • In the regression problem, Y is quantitative (e.g price, blood pressure) • In the classification problem, Y takes values in a finite, unordered set (survived/died, digit 0-9, cancer class of tissue sample) • We have training data (x1 , y1 ), . . . , (xN , yN ). These are observations (examples, instances) of these measurements. 15 ESL Chapter 1 — Introduction Trevor Hastie and Rob Tibshirani Objectives On the basis of the training data we would like to: • Accurately predict unseen test cases • Understand which inputs affect the outcome, and how • Assess the quality of our predictions and inferences 16 ESL Chapter 1 — Introduction Trevor Hastie and Rob Tibshirani Philosophy • It is important to understand the ideas behind the various techniques, in order to know how and when to use them. • One has to understand the simpler methods first, in order to grasp the more sophisticated ones. • It is important to accurately assess the performance of a method, to know how well or how badly it is working [simpler methods often perform as well as fancier ones!] • This is an exciting research area, having important applications in science, industry and finance. 17 ESL Chapter 1 — Introduction Trevor Hastie and Rob Tibshirani Unsupervised learning • No outcome variable, just a set of predictors (features) measured on a set of samples. • objective is more fuzzy — find groups of samples that behave similarly, find features that behave similarly, find linear combinations of features with the most variation. • difficult to know how well your are doing. • different from supervised learning, but can be useful as a pre-processing step for supervised learning 18 ESL Chapter 1 — Introduction Trevor Hastie and Rob Tibshirani The Netflix prize • competition started in October 2006. Training data is ratings for 18, 000 movies by 400, 000 Netflix customers, each rating between 1 and 5 • training data is very sparse— about 98% missing • objective is to predict the rating for a set of 1 million customer-movie pairs that are missing in the training data • Netflix’s original algorithm achieved a root MSE of 0.953. The first team to achieve a 10% improvement wins 1 million dollars. • is this a supervised or unsupervised problem? 19 ESL Chapter 1 — Introduction Trevor Hastie and Rob Tibshirani Grand Prize: one million dollars, if beat Netflix’s RMSE by 10%. Competition ends Sep 21, 2009 after ≈ 3 years, two leaders, 41305 teams! Winner is BellKor’s Pragmatic Chaos. 20...
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This note was uploaded on 01/14/2014 for the course STATS 315A taught by Professor Tibshirani,r during the Winter '10 term at Stanford.

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