Summary

In this chapter, we defined machine learning as the design of programs that can improve their performance at a task by learning from experience. We discussed the spectrum of supervision in experience. At one end is supervised learning, in which a program learns from inputs that are labeled with their corresponding outputs. Unsupervised learning, in which the program must discover structure in only unlabeled inputs, is at the opposite end of the spectrum. Semi-supervised approaches make use of both labeled and unlabeled training data.

Next we discussed common types of machine learning tasks and reviewed examples of each. In classification tasks the program predict the value of a discrete response variable from the observed explanatory variables. In regression tasks the program must predict the value of a continuous response variable from the explanatory variables. Unsupervised learning tasks include clustering, in which observations are organized into groups according to some similarity measure, and dimensionality reduction, which reduces a set of explanatory variables to a smaller set of synthetic features that retain as much information as possible. We also reviewed the bias-variance trade-off and discussed common performance measures for different machine learning tasks.

In this chapter we discussed the history, goals, and advantages of scikit-learn. Finally, we prepared our development environment by installing scikit-learn and other libraries that are commonly used in conjunction with it. In the next chapter we will discuss a simple model for regression tasks, and build our first machine learning model with scikit-learn.