7 Supervised Machine Learning

7.9 Review

The following are the main points you should have learned from this chapter:

  • Learning is the ability of an agent to improve its behavior based on experience.

  • Supervised learning is the problem of predicting the target of a new input, given a set of input–target pairs.

  • Given some training examples, an agent builds a representation that can be used for new predictions.

  • Linear classifiers and decision tree classifiers are representations which are the basis for more sophisticated models.

  • Overfitting occurs when a prediction fits the training set well but does not fit the test set or future predictions.