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

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

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

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

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

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