An Introduction to Statistical Learning with Applications in R, Lesson 01

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Statistic Learning

A. Tools for understanding data

• Supervised
  

            Building a statistical model for predicting, or estimating, an output based on one or more inputs
            Applications: business, medicine, astrophysics, and public policy

• Unsupervised
  

            There are inputs but no supervising output

B. Three data sets

• Wage Data: to understand the association between an employee’s age and education, as well as the calendar year, on his wage
• Stock Market Data: to predict whether the index will increase or decrease on a given day using the past 5 days’ percentage changes in the index
• Gene Expression Data: to understand which types of customers are similar to each other by grouping individuals according to their observed characteristics
  

C. Three problem types

• A regression problem: predicting a continuous or quantitative output value
• A classification problem: predicting a non-numerical value such as a categorical or qualitative output
• A clustering problem: not trying to predict an output variable
  

D. Brief history

• linear regression: 19th century, Legendre and Gauss  
• linear discriminant analysis: 1936, Fisher
• logistic regression: 1940s
• generalized linear models: early 1970s, Nelder and Wedderburn  
• classification and regression trees: mid 1980s, Breiman, Friedman, Olshen and Stone
• generalized additive models: 1986, Hastie and Tibshirani
• machine learning
  

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