An introduction to Statistical Learning

A book by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor.

• Statistical learning refers to a set of tools to understand data.
• Supervised statistical learning invlves building a model for predicting or estimating an output based on inputs.
• In unsupervised there are inputs but no supervising output. We learn the relationships from the data. Wage data
• What is the association between an employee’s age, education and year, on their wage?
• Ideally we should predict wage in a way that accounts for the non-linear relationship between wage and age. Stock Market Data
• Sometimes we want to predict a non-numerical value, categorical or qualitative.
• The goal is to predict if the index will decrease or increase. Gene Expression Data
• We may want to know what types of customers are similar to each other. This is a clustering problem.
• Deciding the number of clusters is often a difficult problem.

  A brief history of statistical learning

• Linear regression is used to predict quantitative values.
• In this book:
  • n is the number of data points.
  • p is the number of variables available.

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