foundations of computational agents
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.