15 Classifying data with logistic regression

This chapter covers

  • Understanding classification problems and measuring classifiers
  • Finding decision boundaries to classify two kinds of data
  • Approximating classified data sets with logistic functions
  • Writing a cost function for logistic regression
  • Carrying out gradient descent to find a logistic function of best fit

One of the most important classes of problems in machine learning is classification, which we’ll focus on in the last two chapters of this book. A classification problem is one where we’ve got one or more pieces of raw data, and we want to say what kind of object each one represents. For instance, we might want an algorithm to look at the data of all email messages entering our inbox ...

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